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ORCIDTransactions on Machine Learning Research, Volume 2025
Volume 2025, 2025
- Benjamin Cohen-Wang, Joshua Vendrow, Aleksander Madry:
Ask Your Distribution Shift if Pre-Training is Right for You. - Shubhankar Gupta, Saksham Sharma, Suresh Sundaram:
Reward-based Autonomous Online Learning Framework for Resilient Cooperative Target Monitoring using a Swarm of Robots. - Wenhao Lu, Xufeng Zhao, Josua Spisak, Jae Hee Lee, Stefan Wermter:
Mental Modelling of Reinforcement Learning Agents by Language Models. - Debarshi Brahma, Anuska Roy, Soma Biswas:
Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts. - Myeongho Jeon, Suhwan Choi, Hyoje Lee, Teresa Yeo:
An Analysis of Model Robustness across Concurrent Distribution Shifts. - Madison Cooley, Varun Shankar, Mike Kirby, Shandian Zhe:
Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases. - Weijian Luo:
Diff-Instruct++: Training One-step Text-to-image Generator Model to Align with Human Preferences. - David Chiang:
Transformers in Uniform TC0. - Steven Jecmen, Nihar B. Shah, Fei Fang, Leman Akoglu:
On the Detection of Reviewer-Author Collusion Rings From Paper Bidding. - Yuan Zang, Tian Yun, Hao Tan, Trung Bui, Chen Sun:
Pre-trained Vision-Language Models Learn Discoverable Visual Concepts. - Peihong Yu, Manav Mishra, Alec Koppel, Carl E. Busart, Priya Narayan, Dinesh Manocha, Amrit Singh Bedi, Pratap Tokekar:
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning. - Tim Z. Xiao, Johannes Zenn, Robert Bamler:
A Note on Generalization in Variational Autoencoders: How Effective Is Synthetic Data and Overparameterization? - Dominik Fay, Sebastian Mair, Jens Sjölund:
Personalized Privacy Amplification via Importance Sampling. - Alexander Larionov, Niall M. Adams, Kevin N. Webster:
Investigating the impact of missing value handling on Boosted trees and Deep learning for Tabular data: A Claim Reserving case study. - Franka Bause, Fabian Jogl, Patrick Indri, Tamara Drucks, David Penz, Nils Morten Kriege, Thomas Gärtner, Pascal Welke, Maximilian Thiessen:
Maximally Expressive GNNs for Outerplanar Graphs. - Shruti P. Maralappanavar, Prashant Khanduri, B. N. Bharath:
Linear Convergence of Decentralized FedAvg for PL Objectives: The Interpolation Regime. - Yihang Gao, Chuanyang Zheng, Enze Xie, Han Shi, Tianyang Hu, Yu Li, Michael Ng, Zhenguo Li, Zhaoqiang Liu:
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures. - Yulei Qin, Yuncheng Yang, Pengcheng Guo, Gang Li, Hang Shao, Yuchen Shi, Zihan Xu, Yun Gu, Ke Li, Xing Sun:
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models. - Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Joshua M. Susskind, Navdeep Jaitly, Shuangfei Zhai:
Improving GFlowNets for Text-to-Image Diffusion Alignment. - Sahil Verma, Gantavya Bhatt, Avi Schwarzschild, Soumye Singhal, Arnav Mohanty Das, Chirag Shah, John P. Dickerson, Pin-Yu Chen, Jeff A. Bilmes:
Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective. - Manu Gaur, Darshan Singh S, Makarand Tapaswi:
No Detail Left Behind: Revisiting Self-Retrieval for Fine-Grained Image Captioning. - Miles Everett, Mingjun Zhong, Georgios Leontidis:
Masked Capsule Autoencoders. - Suryam Arnav Kalra, Arindam Biswas, Pabitra Mitra, Biswajit Basu:
Sparse Neural Architectures via Deterministic Ramanujan Graphs. - Chloe Loughridge, Qinyi Sun, Seth Ahrenbach, Federico Cassano, Chuyue Sun, Ying Sheng, Anish Mudide, Md Rakib Hossain Misu, Nada Amin, Max Tegmark:
DafnyBench: A Benchmark for Formal Software Verification. - Clément Bonet, Kimia Nadjahi, Thibault Séjourné, Kilian Fatras, Nicolas Courty:
Slicing Unbalanced Optimal Transport. - Amitangshu Mukherjee, Timur Ibrayev, Kaushik Roy:
On Inherent Adversarial Robustness of Active Vision Systems. - Marco Paul E. Apolinario, Kaushik Roy:
S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks. - Tobias Leemann, Alina Fastowski, Felix Pfeiffer, Gjergji Kasneci:
Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers. - Yifei He, Yuzheng Hu, Yong Lin, Tong Zhang, Han Zhao:
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic. - Peter Matthew Jacobs, Lekha Patel, Anirban Bhattacharya, Debdeep Pati:
Minimax Posterior Contraction Rates for Unconstrained Distribution Estimation on [0, 1]d under Wasserstein Distance. - Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio, Hossein Talebi, Vishal M. Patel, Peyman Milanfar:
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models. - Bingxin Zhou, Outongyi Lv, Jing Wang, Xiang Xiao, Weishu Zhao:
ODNet: Opinion Dynamics-Inspired Neural Message Passing for Graphs and Hypergraphs. - Seth Neel:
PRIMO: Private Regression in Multiple Outcomes. - Tobias Fuchs, Florian Kalinke, Klemens Böhm:
Partial-Label Learning with a Reject Option. - Stefano Peluchetti:
BM2: Coupled Schrödinger Bridge Matching. - Vidhi Lalchand, Anna-Christina Eilers:
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative model for Quasar spectra. - Pedro Cisneros-Velarde, Zhijie Chen, Sanmi Koyejo, Arindam Banerjee:
Optimization and Generalization Guarantees for Weight Normalization. - Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, Antoine Souloumiac:
Optimal Transport for Domain Adaptation through Gaussian Mixture Models. - Zidu Yin, Zhen Zhang, Dong Gong, Stefano V. Albrecht, Javen Qinfeng Shi:
Highway Graph to Accelerate Reinforcement Learning. - Saeideh Ghanbari Azar, Lorenzo Tronchin, Attila Simkó, Tufve Nyholm, Tommy Löfstedt:
From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning. - Arman Rahbar, Niklas Åkerblom, Morteza Haghir Chehreghani:
Cost-Efficient Online Decision Making: A Combinatorial Multi-Armed Bandit Approach. - Yikai Zhang, Jiahe Lin, Fengpei Li, Songzhu Zheng, Anant Raj, Anderson Schneider, Yuriy Nevmyvaka:
Reweighting Improves Conditional Risk Bounds. - Lei Zhao, Lin Cai, Wu-Sheng Lu:
Federated Learning with Efficient Local Adaptation for Realized Volatility Prediction. - Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, Thomas B. Schön:
Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations. - Marc T. Law, Karsten Kreis, Haggai Maron:
Directed Graph Generation with Heat Kernels. - Shuai Zhao, Meihuizi Jia, Zhongliang Guo, Leilei Gan, Xiaoyu Xu, Xiaobao Wu, Jie Fu, Yichao Feng, Fengjun Pan, Anh Tuan Luu:
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models. - Nimrod Berman, Eitan Kosman, Dotan Di Castro, Omri Azencot:
Reviving Life on the Edge: Joint Score-Based Graph Generation of Rich Edge Attributes. - Nayoung Kim, Minsu Kim, Sungsoo Ahn, Jinkyoo Park:
Decoupled Sequence and Structure Generation for Realistic Antibody Design. - Nicolas Boizard, Kevin El Haddad, Céline Hudelot, Pierre Colombo:
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs. - Adarsh Kappiyath, Anmol Garg, Ramya Hebbalaguppe, Prathosh AP:
Lifelong Learning in StyleGAN through Latent Subspaces. - Leah Bar, Boaz Lerner, Nir Darshan, Rami Ben-Ari:
Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval. - Alejandro Guerra-Manzanares, Farah Shamout:
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks. - Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu:
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. - Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart:
A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges. - Subba Reddy Oota, Zijiao Chen, Manish Gupta, Bapi Raju Surampudi, Gaël Jobard, Frédéric Alexandre, Xavier Hinaut:
Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey). - Eugene A. Golikov:
A Generalization Bound for Nearly-Linear Networks. - Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda, Narges Razavian:
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling. - Hiroyuki Sakai, Hideaki Iiduka:
A general framework of Riemannian adaptive optimization methods with a convergence analysis. - Tal Reiss, Yedid Hoshen:
An Attribute-based Method for Video Anomaly Detection. - Chun-Yin Huang, Ruinan Jin, Can Zhao, Daguang Xu, Xiaoxiao Li:
Federated Learning on Virtual Heterogeneous Data with Local-Global Dataset Distillation. - Oskar Nordenfors, Fredrik Ohlsson, Axel Flinth:
Optimization Dynamics of Equivariant and Augmented Neural Networks. - Paul Brunzema, Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe:
Event-Triggered Time-Varying Bayesian Optimization. - Thomas Pethick, Parameswaran Raman, Lenon Minorics, Mingyi Hong, Shoham Sabach, Volkan Cevher:
νSAM: Memory-Efficient Sharpness-Aware Minimization via Nuclear Norm Constraints. - Netta Ollikka, Amro Abbas, Andrea Perin, Markku Kilpeläinen, Stéphane Deny:
A comparison between humans and AI at recognizing objects in unusual poses. - David Mueller, Mark Dredze, Nicholas Andrews:
Can Optimization Trajectories Explain Multi-Task Transfer? - Cen-You Li, Olaf Dünnbier, Marc Toussaint, Barbara Rakitsch, Christoph Zimmer:
Global Safe Sequential Learning via Efficient Knowledge Transfer. - Stanislas Strasman, Antonio Ocello, Claire Boyer, Sylvain Le Corff, Vincent Lemaire:
An analysis of the noise schedule for score-based generative models. - Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx:
On the Properties and Estimation of Pointwise Mutual Information Profiles. - Luciana Ferrer, Daniel Ramos:
Evaluating Posterior Probabilities: Decision Theory, Proper Scoring Rules, and Calibration. - Koki Okajima, Tomoyuki Obuchi:
Transfer Learning in ℓ1 Regularized Regression: Hyperparameter Selection Strategy based on Sharp Asymptotic Analysis. - Ruisu Zhang, Yicong Chen, Kangwook Lee:
Improving CLIP Counting Accuracy via Parameter-Efficient Fine-Tuning. - Chuanhui Liu, Xiao Wang:
Doubly Robust Conditional VAE via Decoder Calibration: An Implicit KL Annealing Approach. - Luca Simi:
A Scalable Approach for Mapper via Efficient Spatial Search. - Alexey Kravets, Vinay P. Namboodiri:
Zero-shot CLIP Class Forgetting via Text-image Space Adaptation. - Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim:
Fairness Through Matching. - Sai Saketh Rambhatla, Ishan Misra:
SelfEval: Leveraging discriminative nature of generative models for evaluation. - Zhiyu Guo, Hidetaka Kamigaito, Taro Watanabe:
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models. - Prithviraj Tarale, Edward A. Rietman, Hava T. Siegelmann:
Distributed Multi-Agent Lifelong Learning. - Yu Wang, Chi Han, Tongtong Wu, Xiaoxin He, Wangchunshu Zhou, Nafis Sadeq, Xiusi Chen, Zexue He, Wei Wang, Gholamreza Haffari, Heng Ji, Julian J. McAuley:
Towards LifeSpan Cognitive Systems. - Zhuoran Yu, Chenchen Zhu, Sean Culatana, Raghuraman Krishnamoorthi, Fanyi Xiao, Yong Jae Lee:
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images. - Tanguy Bosser, Souhaib Ben Taieb:
Preventing Conflicting Gradients in Neural Marked Temporal Point Processes. - Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi:
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE. - Chao-Kai Chiang, Masashi Sugiyama:
Unified Risk Analysis for Weakly Supervised Learning. - Dun Zeng, Zenglin Xu, Yu Pan, Xu Luo, Qifan Wang, Xiaoying Tang:
Enhanced Federated Optimization: Adaptive Unbiased Client Sampling with Reduced Variance. - Riccardo Majellaro, Jonathan Collu, Aske Plaat, Thomas M. Moerland:
Explicitly Disentangled Representations in Object-Centric Learning. - Nicholas Krämer:
Numerically Robust Fixed-Point Smoothing Without State Augmentation. - Joseph Paul Cohen, Louis Blankemeier, Akshay S. Chaudhari:
Identifying Spurious Correlations using Counterfactual Alignment. - Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian:
Semantic Alignment for Prompt-Tuning in Vision Language Models. - Georgios Vlassis, David Belius, Volodymyr Fomichov:
A thorough reproduction and evaluation of µP. - Zhuo Zhi, Yuxuan Sun, Qiangqiang Wu, Ziquan Liu, Miguel R. D. Rodrigues:
Wasserstein Modality Alignment Makes Your Multimodal Transformer More Robust. - Ilana Sebag, Muni Sreenivas Pydi, Jean-Yves Franceschi, Alain Rakotomamonjy, Mike Gartrell, Jamal Atif, Alexandre Allauzen:
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance. - Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, Soheil Feizi:
Can AI-Generated Text be Reliably Detected? Stress Testing AI Text Detectors Under Various Attacks. - Nauman Ahad, Mark A. Davenport, Eva L. Dyer:
Time Series Domain Adaptation via Channel-Selective Representation Alignment. - Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla:
ExCeL: Combined Extreme and Collective Logit Information for Out-of-Distribution Detection. - Meher Chaitanya, Kshitijaa Jaglan, Ulrik Brandes:
Adjacency Search Embeddings. - Michal Derezinski:
Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches. - Bolian Li, Ruqi Zhang:
Making Reliable and Flexible Decisions in Long-tailed Classification. - Georgios Sidiropoulos, Samarth Bhargav, Panagiotis Eustratiadis, Evangelos Kanoulas:
Multivariate Dense Retrieval: A Reproducibility Study under a Memory-limited Setup. - Shayan Mohajer Hamidi, Linfeng Ye:
Distributed Quasi-Newton Method for Fair and Fast Federated Learning. - Spandan Madan, Tomotake Sasaki, Hanspeter Pfister, Tzu-Mao Li, Xavier Boix:
In-distribution adversarial attacks on object recognition models using gradient-free search. - Hongyi Ling, Zhimeng Jiang, Na Zou, Shuiwang Ji:
Counterfactual Fairness on Graphs: Augmentations, Hidden Confounders, and Identifiability. - Anastasis Kratsios, Haitz Sáez de Ocáriz Borde, Takashi Furuya, Marc T. Law:
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts. - Zhepeng Cen, Yao Liu, Siliang Zeng, Pratik Chaudhari, Huzefa Rangwala, George Karypis, Rasool Fakoor:
Bridging the Training-Inference Gap in LLMs by Leveraging Self-Generated Tokens. - Jieru Mei, Liang-Chieh Chen, Alan L. Yuille, Cihang Xie:
SPFormer: Enhancing Vision Transformer with Superpixel Representation. - Yuzhu Mao, Zihao Zhao, Siqi Ping, Yang Liu, Wenbo Ding:
Enhancing Parameter Efficiency and Generalization in Large Models: A Regularized and Masked Low-Rank Adaptation Approach. - Carlos Mougan, Klaus Broelemann, Gjergji Kasneci, Thanassis Tiropanis, Steffen Staab:
Explanation Shift: How Did the Distribution Shift Impact the Model? - Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer G. Dy:
ADAPT to Robustify Prompt Tuning Vision Transformers. - Xinyu Tang, Ashwinee Panda, Milad Nasr, Saeed Mahloujifar, Prateek Mittal:
Private Fine-tuning of Large Language Models with Zeroth-order Optimization. - Chao Zhou, Huishuai Zhang, Jiang Bian, Weiming Zhang, Nenghai Yu:
©Plug-in Authorization for Human Copyright Protection in Text-to-Image Model. - Boyi Li, Philipp Wu, Pieter Abbeel, Jitendra Malik:
Interactive Task Planning with Language Models. - Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason, Sarunas Girdzijauskas:
On the effects of similarity metrics in decentralized deep learning under distribution shift. - Gabriel Dubé, Mario Marchand:
Shapley Values of Structured Additive Regression Models and Application to RKHS Weightings of Functions. - Florian Kalinke, Marco Heyden, Georg Gntuni, Edouard Fouché, Klemens Böhm:
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection. - Michele Miranda, Elena Sofia Ruzzetti, Andrea Santilli, Fabio Massimo Zanzotto, Sébastien Bratières, Emanuele Rodolà:
Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions. - Muhammed Fatih Balin, Dominique LaSalle, Ümit V. Çatalyürek:
Cooperative Minibatching in Graph Neural Networks. - Nicholas Bai, Rahul A. Iyer, Tuomas P. Oikarinen, Akshay R. Kulkarni, Tsui-Wei Weng:
Interpreting Neurons in Deep Vision Networks with Language Models. - Noureddine Henka, Mohamad Assaad, Sami Tazi:
Mixture Degree-Corrected Stochastic Block Model for Multi-Group Community Detection in Multiplex Graphs. - Sebastian Wankerl, Jan Pfister, Andrzej Dulny, Gerhard Götz, Andreas Hotho:
Identifying Axiomatic Mathematical Transformation Steps using Tree-Structured Pointer Networks. - Konstantin Mishchenko, Rustem Islamov, Eduard Gorbunov, Samuel Horváth:
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity. - Ahmad-Reza Ehyaei, Golnoosh Farnadi, Samira Samadi:
Bridging Causality, Individual Fairness, and Adversarial Robustness in the Absence of Structural Causal Model. - Michele Caprio, David Stutz, Shuo Li, Arnaud Doucet:
Conformalized Credal Regions for Classification with Ambiguous Ground Truth. - Geri Skenderi, Hang Li, Jiliang Tang, Marco Cristani:
Graph-level Representation Learning with Joint-Embedding Predictive Architectures. - Zidan Wang, Rui Shen, Bradly C. Stadie:
Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs. - Bas van der Heijden, Jens Kober, Robert Babuska, Laura Ferranti:
REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation. - Travis E. Gibson, Sawal Acharya, Anjali Parashar, Joseph E. Gaudio, Anuradha Annaswamy:
On the stability of gradient descent with second order dynamics for time-varying cost functions. - Motasem Alfarra, Alvaro H. C. Correia, Bernard Ghanem, Christos Louizos:
Test-Time Adaptation with Source Based Auxiliary Tasks. - Saptarshi Chakraborty:
Minimax Lower Bounds for Estimating Distributions on Low-dimensional Spaces. - Saleh Gholam Zadeh, Vaisakh Shaj, Patrick Jahnke, Gerhard Neumann, Tim Breitenbach:
Towards Measuring Predictability: To which extent data-driven approaches can extract deterministic relations from data exemplified with time series prediction and classification. - Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo:
Iterated Q-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning. - Chandramouli Shama Sastry, Mahdi Gilany, Kry Yik-Chau Lui, Martin Magill, Alexander Pashevich:
DeepRRTime: Robust Time-series Forecasting with a Regularized INR Basis. - Jackson Petty, Sjoerd van Steenkiste, Tal Linzen:
How Does Code Pretraining Affect Language Model Task Performance? - Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Israfil Bahceci, Akram Bin Sediq, Hamza Umit Sokun, Nicolas Papernot:
Selective Prediction via Training Dynamics. - Arash Behboodi, Gabriele Cesa:
On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing. - Minguk Jang, Hye Won Chung:
Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation. - Hikari Otsuka, Daiki Chijiwa, Ángel López García-Arias, Yasuyuki Okoshi, Kazushi Kawamura, Thiem Van Chu, Daichi Fujiki, Susumu Takeuchi, Masato Motomura:
Partially Frozen Random Networks Contain Compact Strong Lottery Tickets. - Jiazheng Li, Jundong Li, Chuxu Zhang:
Instance-Aware Graph Prompt Learning. - Savvas Melidonis, Yiming Xi, Konstantinos C. Zygalakis, Yoann Altmann, Marcelo Pereyra:
Score-Based Denoising Diffusion Models for Photon-Starved Image Restoration Problems. - Haonan Wang, Qian Liu, Chao Du, Tongyao Zhu, Cunxiao Du, Kenji Kawaguchi, Tianyu Pang:
When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training. - Pengyun Wang, Yadi Cao, Chris Russell, Yanxin Shen, Junyu Luo, Ming Zhang, Siyu Heng, Xiao Luo:
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation. - Ibrahim Serouis, Florence Sèdes:
Towards context and domain-aware algorithms for scene analysis. - Luca Butera, Giovanni de Felice, Andrea Cini, Cesare Alippi:
On the Regularization of Learnable Embeddings for Time Series Forecasting. - Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li:
LLaVA-OneVision: Easy Visual Task Transfer. - Zhi Chen, Yufan Ren, Tong Zhang, Zheng Dang, Wenbing Tao, Sabine Süsstrunk, Mathieu Salzmann:
Adaptive Multi-step Refinement Network for Robust Point Cloud Registration. - Liran Nochumsohn, Omri Azencot:
Data Augmentation Policy Search for Long-Term Forecasting. - Ya Song, Laurens Bliek, Yaoxin Wu, Yingqian Zhang:
Enhancing Remaining Useful Life Prediction with Ensemble Multi-Term Fourier Graph Neural Networks. - Hussein Mozannar, Valerie Chen, Mohammed Alsobay, Subhro Das, Sebastian Zhao, Dennis Wei, Manish Nagireddy, Prasanna Sattigeri, Ameet Talwalkar, David A. Sontag:
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers. - Anna Hedström, Philine Lou Bommer, Thomas F. Burns, Sebastian Lapuschkin, Wojciech Samek, Marina M.-C. Höhne:
Evaluating Interpretable Methods via Geometric Alignment of Functional Distortions. - Giovanni Luca Marchetti, Gabriele Cesa, Kumar Pratik, Arash Behboodi:
Neural Lattice Reduction: A Self-Supervised Geometric Deep Learning Approach. - Neil Ashtekar, Jingxi Zhu, Vasant G. Honavar:
Class Incremental Learning from First Principles: A Review. - Aymene Mohammed Bouayed, Samuel Deslauriers-Gauthier, Adrian Iacovelli, David Naccache:
CNN Interpretability with Multivector Tucker Saliency Maps for Self-Supervised Models. - Thibault de Surrel, Sylvain Chevallier, Fabien Lotte, Florian Yger:
Geometry-Aware visualization of high dimensional Symmetric Positive Definite matrices. - Sharmita Dey, Benjamin Paassen, Sarath Ravindran Nair, Sabri Boughorbel, Arndt F. Schilling:
Continual Learning from Simulated Interactions via Multitask Prospective Rehearsal for Bionic Limb Behavior Modeling. - Ali Shirali, Moritz Hardt:
What Makes ImageNet Look Unlike LAION. - Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal:
Explaining Explainability: Recommendations for Effective Use of Concept Activation Vectors. - Antonios Valkanas, Yuening Wang, Yingxue Zhang, Mark Coates:
Personalized Negative Reservoir for Incremental Learning in Recommender Systems. - Lorenzo Loconte, Antonio Mari, Gennaro Gala, Robert Peharz, Cassio de Campos, Erik Quaeghebeur, Gennaro Vessio, Antonio Vergari:
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)? - Cullen Anderson, Jeff M. Phillips:
Robust High-Dimensional Mean Estimation With Low Data Size, an Empirical Study. - Viraj Shah, Svetlana Lazebnik, Julien Philip:
JoIN: Joint GANs Inversion for Intrinsic Image Decomposition. - Hikaru Umeda, Hideaki Iiduka:
Increasing Both Batch Size and Learning Rate Accelerates Stochastic Gradient Descent. - Lev Telyatnikov, Maria Sofia Bucarelli, Guillermo Bernárdez, Olga Zaghen, Simone Scardapane, Pietro Lio:
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design. - Yancheng Wang, Changyu Liu, Yingzhen Yang:
Diffusion on Graph: Augmentation of Graph Structure for Node Classification. - Haoyun Yin, Yixuan Qiu, Xiao Wang:
Wasserstein Coreset via Sinkhorn Loss. - Mahrokh Ghoddousi Boroujeni, Andreas Krause, Giancarlo Ferrari-Trecate:
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach. - Simon Dufort-Labbé, Pierluca D'Oro, Evgenii Nikishin, Irina Rish, Pierre-Luc Bacon, Razvan Pascanu, Aristide Baratin:
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons. - Yuki Takezawa, Ryoma Sato, Han Bao, Kenta Niwa, Makoto Yamada:
Necessary and Sufficient Watermark for Large Language Models. - Sara Venturini, Marianna De Santis, Jordan Patracone, Martin Schmidt, Francesco Rinaldi, Saverio Salzo:
Relax and penalize: a new bilevel approach to mixed-binary hyperparameter optimization. - Stefano Bruno, Ying Zhang, Dongyoung Lim, Ömer Deniz Akyildiz, Sotirios Sabanis:
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates. - Joel Jonsson, Bevan Leslie Cheeseman, Ivo F. Sbalzarini:
APR-CNN: Convolutional Neural Networks for the Adaptive Particle Representation of Large Microscopy Images. - Eric Tang, Bangding Yang, Xingyou Song:
Understanding LLM Embeddings for Regression. - Rundong Luo, Hong-Xing Yu, Jiajun Wu:
Unsupervised Discovery of Object-Centric Neural Fields. - Yuki Ichihara, Yuu Jinnai, Tetsuro Morimura, Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Eiji Uchibe:
Evaluation of Best-of-N Sampling Strategies for Language Model Alignment. - Harsh Raj, Vipul Gupta, Domenic Rosati, Subhabrata Majumdar:
Improving Consistency in Large Language Models through Chain of Guidance. - Michal Lewandowski, Hamid Eghbalzadeh, Bernhard Heinzl, Raphael Pisoni, Bernhard Alois Moser:
On Space Folds of ReLU Neural Networks. - Astrit Tola, Jack Myrick, Baris Coskunuzer:
PROXI: Challenging the GNNs for Link Prediction. - Philippe Formont, Hugo Jeannin, Pablo Piantanida, Ismail Ben Ayed:
A Strong Baseline for Molecular Few-Shot Learning. - Xiangru Jian, Xinjian Zhao, Wei Pang, Chaolong Ying, Yimu Wang, Yaoyao Xu, Tianshu Yu:
Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning. - Margherita Mele, Roberto Menichetti, Alessandro Ingrosso, Raffaello Potestio:
Density of states in neural networks: an in-depth exploration of learning in parameter space. - Peter Shaw, James Cohan, Jacob Eisenstein, Kenton Lee, Jonathan Berant, Kristina Toutanova:
ALTA: Compiler-Based Analysis of Transformers. - David Brandfonbrener, Nikhil Anand, Nikhil Vyas, Eran Malach, Sham M. Kakade:
Loss-to-Loss Prediction: Scaling Laws for All Datasets. - Fang Wu, Stan Z. Li:
Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling. - Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang:
The 2023 Foundation Model Transparency Index. - Sucheng Ren, Hongru Zhu, Chen Wei, Yijiang Li, Alan L. Yuille, Cihang Xie:
ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning. - Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets, Tim Verdonck, Wouter Verbeke:
Using representation balancing to learn conditional-average dose responses from clustered data. - Sheng Cheng, Deqian Kong, Jianwen Xie, Kookjin Lee, Ying Nian Wu, Yezhou Yang:
Latent Space Energy-based Neural ODEs. - Carlos E. Luis, Alessandro G. Bottero, Julia Vinogradska, Felix Berkenkamp, Jan Peters:
Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability. - Francois Caron, Fadhel Ayed, Paul Jung, Hoil Lee, Juho Lee, Hongseok Yang:
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning. - Weizhi Lu, Zhongzheng Li, Mingrui Chen, Weiyu Li:
The Sparse Matrix-Based Random Projection: A Study of Binary and Ternary Quantization. - Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, Ye Wang:
On Memorization in Diffusion Models. - Piyush Tiwary, Atri Guha, Subhodip Panda, Prathosh AP:
Adapt then Unlearn: Exploring Parameter Space Semantics for Unlearning in Generative Adversarial Networks. - Kieran A. Murphy, Sam Dillavou, Danielle S. Bassett:
Comparing the information content of probabilistic representation spaces. - Martha Lewis, Melanie Mitchell:
Evaluating the Robustness of Analogical Reasoning in Large Language Models. - Eliav Mor, Yair Carmon:
An Analytical Model for Overparameterized Learning Under Class Imbalance. - Song Wang, Zhen Tan, Yaochen Zhu, Chuxu Zhang, Jundong Li:
Generative Risk Minimization for Out-of-Distribution Generalization on Graphs. - Rudi Coppola, Manuel Mazo Espinosa:
On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals. - Leyla Naz Candogan, Yongtao Wu, Elías Abad-Rocamora, Grigorios Chrysos, Volkan Cevher:
Single-pass Detection of Jailbreaking Input in Large Language Models. - Amer Essakine, Yanqi Cheng, Chun-Wun Cheng, Lipei Zhang, Zhongying Deng, Lei Zhu, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero:
Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey. - Haoyu Wang, Guozheng Ma, Cong Yu, Ning Gui, Linrui Zhang, Zhiqi Huang, Suwei Ma, Yongzhe Chang, Sen Zhang, Li Shen, Xueqian Wang, Peilin Zhao, Dacheng Tao:
Are Large Language Models Really Robust to Word-Level Perturbations? - Julius Ott, Huawei Sun, Enrico Rinaldi, Gianfranco Mauro, Lorenzo Servadei, Robert Wille:
Exploiting Benford's Law for Weight Regularization of Deep Neural Networks. - Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Emiliano De Cristofaro:
The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging. - Duy-Kien Nguyen, Martin R. Oswald, Cees G. M. Snoek:
SimPLR: A Simple and Plain Transformer for Efficient Object Detection and Segmentation. - Adam Fisch, Jacob Eisenstein, Vicky Zayats, Alekh Agarwal, Ahmad Beirami, Chirag Nagpal, Peter Shaw, Jonathan Berant:
Robust Preference Optimization through Reward Model Distillation. - Adrian Remonda, Cole Corbitt Terrell, Eduardo E. Veas, Marc Masana:
Uncertainty-Based Experience Replay for Task-Agnostic Continual Reinforcement Learning. - Shachar Schnapp, Sivan Sabato:
Differentially Private Source-Target Clustering. - Cristian A. Galvis-Florez, Ahmad Farooq, Simo Särkkä:
Provable Quantum Algorithm Advantage for Gaussian Process Quadrature. - Cristina Garbacea, Qiaozhu Mei:
Why is constrained neural language generation particularly challenging? - Houssam Zenati, Alberto Bietti, Matthieu Martin, Eustache Diemert, Pierre Gaillard, Julien Mairal:
Counterfactual Learning of Stochastic Policies with Continuous Actions. - Tim Z. Xiao, Robert Bamler, Bernhard Schölkopf, Weiyang Liu:
Verbalized Machine Learning: Revisiting Machine Learning with Language Models. - Olivier Teytaud, Mariia Zameshina, Tom Sander, Pierre Fernandez, Furong Ye, Laurent Najman, Thomas Bäck, Ismail Labiad:
Lognormal Mutations and their Use in Detecting Surreptitious Fake Images. - Omer Rochman Sharabi, Sacha Lewin, Gilles Louppe:
A Neural Material Point Method for Particle-based Emulation. - Zeyu Yang, Han Yu, Peikun Guo, Khadija Zanna, Xiaoxue Yang, Akane Sano:
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models. - Krishna Acharya, Juba Ziani, Jingyan Wang, Varun Vangala:
Producers Equilibria and Dynamics in Engagement-Driven Recommender Systems. - Prabhu Babu, Petre Stoica, Astha Saini:
Fair principal component analysis (PCA): minorization-maximization algorithms for Fair PCA, Fair Robust PCA and Fair Sparse PCA. - Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan:
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators. - Zach Nussbaum, John Xavier Morris, Andriy Mulyar, Brandon Duderstadt:
Nomic Embed: Training a Reproducible Long Context Text Embedder. - Lan V. Truong:
Global Convergence Rate of Deep Equilibrium Models with General Activations. - Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh:
TACO Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. - Haozhe Liu, Wentian Zhang, Jinheng Xie, Francesco Faccio, Mengmeng Xu, Tao Xiang, Mike Zheng Shou, Juan-Manuel Pérez-Rúa, Jürgen Schmidhuber:
Faster Diffusion Through Temporal Attention Decomposition. - Nikita Malik, Konda Reddy Mopuri:
FaAlGrad: Fairness through Alignment of Gradients across Different Subpopulations. - Jiaqi Wang, Yuhang Zhou, Zhixiong Zhang, Qiguang Chen, Yongqiang Chen, James Cheng:
DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution Generalization. - Minttu Alakuijala, Reginald McLean, Isaac Woungang, Nariman Farsad, Samuel Kaski, Pekka Marttinen, Kai Yuan:
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics. - Zhong Chuang, Yusuke Tanaka, Tomoharu Iwata:
Meta-Learning for Graphs with Heterogeneous Node Attribute Spaces for Few-Shot Edge Predictions. - Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Metalearning Continual Learning Algorithms. - Zijun Wang, Haoqin Tu, Jieru Mei, Bingchen Zhao, Yisen Wang, Cihang Xie:
AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation. - Seyed Moslem Shokrolahi, Il-Min Kim:
Combating Inter-Task Confusion and Catastrophic Forgetting by Metric Learning and Re-Using a Past Trained Model. - Yilun Kong, Hangyu Mao, Qi Zhao, Bin Zhang, Jingqing Ruan, Li Shen, Yongzhe Chang, Xueqian Wang, Rui Zhao, Dacheng Tao:
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning. - Amadou S. Sangare, Nicolas Dunou, Jhony H. Giraldo, Fragkiskos D. Malliaros:
A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning. - Tian Xie, Jifan Zhang, Haoyue Bai, Robert D. Nowak:
Deep Active Learning in the Open World. - Roozbeh Yousefzadeh, Xuenan Cao:
A Lean Dataset for International Math Olympiad: Small Steps towards Writing Math Proofs for Hard Problems. - Mohsen Tabejamaat, Farzaneh Etminani, Mattias Ohlsson:
Cycle Conditioning for Robust Representation Learning from Categorical Data. - Isay Katsman, Anna Gilbert:
Shedding Light on Problems with Hyperbolic Graph Learning. - Weiguo Gao, Ming Li:
Evolution of Discriminator and Generator Gradients in GAN Training: From Fitting to Collapse. - Jiacheng You, Xinyang Chen, Yu Sun, Weili Guan, Liqiang Nie:
Long Short-Term Imputer: Handling Consecutive Missing Values in Time Series. - Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger:
A Self-Explainable Heterogeneous GNN for Relational Deep Learning. - Hejia Geng, Peng Li:
HoSNNs: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds. - Qi Zhang, Yi Zhou, Shaofeng Zou:
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance. - Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat, Edward S. Hu:
Reset-free Reinforcement Learning with World Models. - Sebastian Gregor Gruber, Francis R. Bach:
Optimizing Estimators of Squared Calibration Errors in Classification. - Tejumade Afonja, Hui-Po Wang, Raouf Kerkouche, Mario Fritz:
DP-2Stage: Adapting Language Models as Differentially Private Tabular Data Generators. - Pavel Rumiantsev, Mark Coates:
Variation Matters: from Mitigating to Embracing Zero-Shot NAS Ranking Function Variation. - Xingmei Lou, Yu Hu, Xiaodong Li:
Learning Linear Polytree Structural Equation Model. - Tobias Bernecker, Ghalia Rehawi, Francesco Paolo Casale, Janine Knauer-Arloth, Annalisa Marsico:
Random Walk Diffusion for Efficient Large-Scale Graph Generation. - Kelly Ramsay, Aukosh Jagannath, Shoja'eddin Chenouri:
An elementary concentration bound for Gibbs measures arising in statistical learning theory. - Giuseppe Serra, Ben Werner, Florian Buettner:
How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning. - Rishi Bommasani, Kevin Klyman, Sayash Kapoor, Shayne Longpre, Betty Xiong, Nestor Maslej, Percy Liang:
The 2024 Foundation Model Transparency Index. - Shenghong Dai, Jy-yong Sohn, Yicong Chen, S. M. Iftekharul Alam, Ravikumar Balakrishnan, Suman Banerjee, Nageen Himayat, Kangwook Lee:
Buffer-based Gradient Projection for Continual Federated Learning. - Roman Bresson, Giannis Nikolentzos, George Panagopoulos, Michail Chatzianastasis, Jun Pang, Michalis Vazirgiannis:
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning. - Daisuke Hatano, Satoshi Hara, Hiromi Arai:
Path-Specific Counterfactual Fairness via Dividend Correction. - Shenao Zhang, Donghan Yu, Hiteshi Sharma, Han Zhong, Zhihan Liu, Ziyi Yang, Shuohang Wang, Hany Hassan Awadalla, Zhaoran Wang:
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment. - Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Milad Cheraghalikhani, Gustavo Adolfo Vargas Hakim, David Osowiechi, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers:
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D. - Qinxun Bai, Steven Rosenberg, Wei Xu:
Generalized Tangent Kernel: A Unified Geometric Foundation for Natural Gradient and Standard Gradient. - Luciana Ferrer:
No Need for Ad-hoc Substitutes: The Expected Cost is a Principled All-purpose Classification Metric. - Phuong Quynh Le, Jörg Schlötterer, Christin Seifert:
Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations. - Vincent Abbott, Gioele Zardini:
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness. - Thomas De Min, Massimiliano Mancini, Stéphane Lathuilière, Subhankar Roy, Elisa Ricci:
Unlearning Personal Data from a Single Image. - Billy Joe Franks, Moshe Eliasof, Semih Cantürk, Guy Wolf, Carola-Bibiane Schönlieb, Sophie Fellenz, Marius Kloft:
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings. - Vinoth Nandakumar, Qiang Qu, Peng Mi, Tongliang Liu:
State space models can express n-gram languages. - Charles Marx, Volodymyr Kuleshov, Stefano Ermon:
Calibrated Probabilistic Forecasts for Arbitrary Sequences. - Thibault Le Sellier de Chezelles, Maxime Gasse, Alexandre Lacoste, Massimo Caccia, Alexandre Drouin, Léo Boisvert, Megh Thakkar, Tom Marty, Rim Assouel, Sahar Omidi Shayegan, Lawrence Keunho Jang, Xing Han Lù, Ori Yoran, Dehan Kong, Frank F. Xu, Siva Reddy, Graham Neubig, Quentin Cappart, Russ Salakhutdinov, Nicolas Chapados:
The BrowserGym Ecosystem for Web Agent Research. - Kristian Schwethelm, Johannes Kaiser, Moritz Knolle, Sarah Lockfisch, Daniel Rueckert, Alexander Ziller:
Visual Privacy Auditing with Diffusion Models. - Zihao Liang, Tianyu Zhou, Zehui Lu, Shaoshuai Mou:
Online Control-Informed Learning. - Pihe Hu, Shaolong Li, Xun Wang, Longbo Huang:
Mixed Sparsity Training: Achieving 4× FLOP Reduction for Transformer Pretraining. - Oisín Nolan, Tristan S. W. Stevens, Wessel L. van Nierop, Ruud van Sloun:
Active Diffusion Subsampling. - Martin Bichler, Davide Legacci, Panayotis Mertikopoulos, Matthias Oberlechner, Bary S. R. Pradelski:
Characterizing the Convergence of Game Dynamics via Potentialness. - Jonas Brusokas, Seshu Tirupathi, Dalin Zhang, Torben Bach Pedersen:
The Time-Energy Model: Selective Time-Series Forecasting Using Energy-Based Models. - Arash Mari Oriyad, Mohammadali Banayeeanzade, Reza Abbasi, Mohammad Hossein Rohban, Mahdieh Soleymani Baghshah:
Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models! - Wenjing Chang, Kay Liu, Philip S. Yu, Jianjun Yu:
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement. - Muheng Li, Ruqi Zhang:
Reheated Gradient-based Discrete Sampling for Combinatorial Optimization. - Manuel Faysse, Patrick Fernandes, Nuno Miguel Guerreiro, António Loison, Duarte M. Alves, Caio Corro, Nicolas Boizard, João Alves, Ricardo Rei, Pedro Henrique Martins, Antoni Bigata Casademunt, François Yvon, André F. T. Martins, Gautier Viaud, Céline Hudelot, Pierre Colombo:
CroissantLLM: A Truly Bilingual French-English Language Model. - Tao Daniel Alter, Raz Lapid, Moshe Sipper:
On the Robustness of Kolmogorov-Arnold Networks: An Adversarial Perspective. - Ashka Shah, Adela Frances DePavia, Nathaniel C. Hudson, Ian T. Foster, Rick Stevens:
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning. - Danil Provodin, Bram van den Akker, Christina Katsimerou, Maurits Clemens Kaptein, Mykola Pechenizkiy:
Rethinking Knowledge Transfer in Learning Using Privileged Information. - Hanyang Wang, Juergen Branke, Matthias Poloczek:
Respecting the limit: Bayesian optimization with a bound on the optimal value. - Yousef El-Laham, Zhongchang Sun, Haibei Zhu, Tucker Balch, Svitlana Vyetrenko:
Variational Neural Stochastic Differential Equations with Change Points. - Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alexander Y. Shestopaloff, Kevin Patrick Murphy:
A unifying framework for generalised Bayesian online learning in non-stationary environments. - Garweet Sresth, Satish Mulleti, Ajit Rajwade:
Unlabelled Compressive Sensing under Sparse Permutation and Prior Information. - Akshay Kumar, Jarvis D. Haupt:
Early Directional Convergence in Deep Homogeneous Neural Networks for Small Initializations. - Justin Kay, Timm Haucke, Suzanne Stathatos, Siqi Deng, Erik Young, Pietro Perona, Sara Beery, Grant Van Horn:
Align and Distill: Unifying and Improving Domain Adaptive Object Detection. - Hana Yahia, Bruno Figliuzzi, Florent Di Meglio, Laurent Gerbaud, Stephane Menand, Mohamed Mahjoub:
Domain Generalization for Time Series: Enhancing Drilling Regression Models for Stick-Slip Index Prediction. - Sai Srinivas Kancheti, Rahul Vigneswaran, Bamdev Mishra, Vineeth N. Balasubramanian:
HARE: Human-in-the-Loop Algorithmic Recourse. - Ramansh Sharma, Varun Shankar:
Ensemble and Mixture-of-Experts DeepONets For Operator Learning. - Shwai He, Daize Dong, Liang Ding, Ang Li:
Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques. - Yaochen Hu, Mai Zeng, Ge Zhang, Pavel Rumiantsev, Liheng Ma, Yingxue Zhang, Mark Coates:
Sparse Decomposition of Graph Neural Networks. - Charles-Étienne Joseph, Benjamin Thérien, Abhinav Moudgil, Boris Knyazev, Eugene Belilovsky:
Meta-learning Optimizers for Communication-Efficient Learning. - Yun Jin Park, Didong Li:
Lower Ricci Curvature for Efficient Community Detection. - Nicolas Drapier, Aladine Chetouani, Aurélien Chateigner:
Enhancing Maritime Trajectory Forecasting via H3 Index and Causal Language Modelling (CLM). - Pranav Jeevan, Amit Sethi:
Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision. - Mikko A. Heikkilä:
On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps. - Asaf Shul, Eliahu Horwitz, Yedid Hoshen:
Distilling Datasets Into Less Than One Image. - José I. Segovia-Martín, Santiago Mazuelas, Anqi Liu:
A Unified View of Double-Weighting for Marginal Distribution Shift. - Brian Matejek, Ashish Gehani, Nathaniel D. Bastian, Daniel J. Clouse, Bradford J. Kline, Susmit Jha:
SAFE-NID: Self-Attention with Normalizing-Flow Encodings for Network Intrusion Detection. - Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Michael Hunter Klein, Vahid Tarokh, David E. Carlson:
Understanding and Robustifying Sub-domain Alignment for Domain Adaptation. - Michael Hagmann, Michael Staniek, Stefan Riezler:
Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation. - Moussa Kassem Sbeyti, Nadja Klein, Azarm Nowzad, Fikret Sivrikaya, Sahin Albayrak:
Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection. - Nolan Simran Dey, J. Eric Taylor, Alexander Wong, Bryan P. Tripp, Graham W. Taylor:
Neuron-based explanations of neural networks sacrifice completeness and interpretability. - Dan Kushnir, Sandeep Silwal:
Cluster Tree for Nearest Neighbor Search. - Christian Dietrich Weilbach, Frank Wood:
Daphne: Multi-Pass Compilation of Probabilistic Programs into Graphical Models and Neural Networks. - Yuki Tsukada, Hideaki Iiduka:
Relationship between Batch Size and Number of Steps Needed for Nonconvex Optimization of Stochastic Gradient Descent using Armijo-Line-Search Learning Rate. - William Chang, Yuanhao Lu:
Multiplayer Information Asymmetric Contextual Bandits. - Weixin Liang, Lili Yu, Liang Luo, Srini Iyer, Ning Dong, Chunting Zhou, Gargi Ghosh, Mike Lewis, Wen-tau Yih, Luke Zettlemoyer, Xi Victoria Lin:
Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models. - Alessandro De Palma, Serge Durand, Zakaria Chihani, François Terrier, Caterina Urban:
On Using Certified Training towards Empirical Robustness. - Tristan S. W. Stevens, Hans Van Gorp, Faik C. Meral, Jun Seob Shin, Jason Yu, Jean-Luc Robert, Ruud van Sloun:
Removing Structured Noise using Diffusion Models. - Woomin Song, Jihoon Tack, Sangwoo Mo, Seunghyuk Oh, Jinwoo Shin:
Sparsified State-Space Models are Efficient Highway Networks. - Seongyoon Kim, Minchan Jeong, Sungnyun Kim, Sungwoo Cho, Sumyeong Ahn, Se-Young Yun:
FedDr+: Stabilizing Dot-regression with Global Feature Distillation for Federated Learning. - Mohammadamin Banayeeanzade, Mahdi Soltanolkotabi, Mohammad Rostami:
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning. - Liyao Jiang, Negar Hassanpour, Mohammad Salameh, Mohan Sai Singamsetti, Fengyu Sun, Wei Lui, Di Niu:
FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting. - Hiroaki Ito, Jiale Yan, Hikari Otsuka, Kazushi Kawamura, Masato Motomura, Thiem Van Chu, Daichi Fujiki:
Uncovering Strong Lottery Tickets in Graph Transformers: A Path to Memory Efficient and Robust Graph Learning. - Chenguang Wang, Zhang-Hua Fu, Pinyan Lu, Tianshu Yu:
Efficient Training of Multi-task Neural Solver for Combinatorial Optimization. - Dahyun Kang, Ahmet Iscen, Eunchan Jo, Sua Choi, Minsu Cho, Cordelia Schmid:
Memory-Modular Classification: Learning to Generalize with Memory Replacement. - Feng Chen, Xinwei Chen, Rong-Jun Qin, Cong Guan, Lei Yuan, Zongzhang Zhang, Yang Yu:
Efficient Multi-Agent Cooperation Learning through Teammate Lookahead. - Aida Mohammadshahi, Yani Ioannou:
What's Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias. - Samuel Stevens, Emily Wenger, Cathy Yuanchen Li, Niklas Nolte, Eshika Saxena, François Charton, Kristin E. Lauter:
Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors. - Brieuc Pinon, Raphaël M. Jungers, Jean-Charles Delvenne:
A limitation on black-box dynamics approaches to Reinforcement Learning. - Arnaud Robert, Aldo A. Faisal, Ciara Pike-Burke:
Posterior Sampling for Reinforcement Learning on Graphs. - Johannes Hog, Raghu Rajan, André Biedenkapp, Noor H. Awad, Frank Hutter, Vu Nguyen:
Meta-learning Population-based Methods for Reinforcement Learning. - Tianle Li, Ge Zhang, Quy Duc Do, Xiang Yue, Wenhu Chen:
Long-context LLMs Struggle with Long In-context Learning. - Ingvar M. Ziemann:
A Vector Bernstein Inequality for Self-Normalized Martingales. - Shivi Dixit, Rishabh Gupta, Qi Zhang:
Decision-Focused Surrogate Modeling for Mixed-Integer Linear Optimization. - Christopher Bülte, Philipp Scholl, Gitta Kutyniok:
Probabilistic neural operators for functional uncertainty quantification. - Niccolò Avogaro, Thomas Frick, Mattia Rigotti, Andrea Bartezzaghi, Filip Janicki, A. Cristiano I. Malossi, Konrad Schindler, Roy Assaf:
Show or Tell? Effectively prompting Vision-Language Models for semantic segmentation. - Ramzi Dakhmouche, Ivan Lunati, M. Hossein Gorji:
Robust Symbolic Regression for Dynamical System Identification. - Niccolò Tosato, Lorenzo Basile, Emanuele Ballarin, Giuseppe de Alteriis, Alberto Cazzaniga, Alessio Ansuini:
Emergent representations in networks trained with the Forward-Forward algorithm. - Jack Foster, Kyle Fogarty, Stefan Schoepf, Zack Dugue, Cengiz Öztireli, Alexandra Brintrup:
An Information Theoretic Approach to Machine Unlearning. - Elena Congeduti, Roberto Rocchetta, Frans A. Oliehoek:
Influence Learning in Complex Systems. - Haoxiang Ma, Shuo Han, Ahmed Hemida, Charles A. Kamhoua, Jie Fu:
Adaptive Incentive Design for Markov Decision Processes with Unknown Rewards. - Christopher Watson, Arjun Krishna, Rajeev Alur, Dinesh Jayaraman:
Illustrated Landmark Graphs for Long-horizon Policy Learning. - Yifei Xiong, Nianqiao P. Ju, Sanguo Zhang:
Simulation-based Bayesian Inference from Privacy Protected Data. - Wenxian Shi, Menghua Wu, Regina Barzilay:
Predicting sub-population specific viral evolution. - Joshua Engels, Logan Riggs Smith, Max Tegmark:
Decomposing The Dark Matter of Sparse Autoencoders. - Bhavya Vasudeva, Puneesh Deora, Christos Thrampoulidis:
Implicit Bias and Fast Convergence Rates for Self-attention. - Siheng Li, Cheng Yang, Taiqiang Wu, Chufan Shi, Yuji Zhang, Xinyu Zhu, Zesen Cheng, Deng Cai, Mo Yu, Lemao Liu, Jie Zhou, Yujiu Yang, Ngai Wong, Xixin Wu, Wai Lam:
A Survey on the Honesty of Large Language Models. - William Chen, Oier Mees, Aviral Kumar, Sergey Levine:
Vision-Language Models Provide Promptable Representations for Reinforcement Learning. - Kefan Su, Zongqing Lu:
f-Divergence Policy Optimization in Fully Decentralized Cooperative MARL. - Yuhang Liu, Zhen Zhang, Dong Gong, Mingming Gong, Biwei Huang, Anton van den Hengel, Kun Zhang, Javen Qinfeng Shi:
Latent Covariate Shift: Unlocking Partial Identifiability for Multi-Source Domain Adaptation. - Yuxuan Shu, Vasileios Lampos:
DeformTime: capturing variable dependencies with deformable attention for time series forecasting. - Shubham Agarwal, Gaurav Sahu, Abhay Puri, Issam H. Laradji, Krishnamurthy Dj Dvijotham, Jason Stanley, Laurent Charlin, Christopher Pal:
LitLLMs, LLMs for Literature Review: Are we there yet? - Shuvendu Roy, Elham Dolatabadi, Arash Afkanpour, Ali Etemad:
Consistency-Guided Asynchronous Contrastive Tuning for Few-Shot Class-Incremental Tuning of Foundation Models. - Simon Weissmann, Sara Klein, Waïss Azizian, Leif Döring:
Almost Sure Convergence of Stochastic Gradient Methods under Gradient Domination. - Ding Zhu, Zhiqun Zuo, Mohammad Mahdi Khalili:
An Efficient Training Algorithm for Models with Block-wise Sparsity. - Paul-Ruben Schlumbom, Eibe Frank:
Revisiting Deep Hybrid Models for Out-of-Distribution Detection. - Shirsha Bose, Mainak Singha, Ankit Jha, Souradeep Mukhopadhyay, Biplab Banerjee:
Meta-Learning to Teach Semantic Prompts for Open Domain Generalization in Vision-Language Models. - Gang Li, Qihang Lin, Ayush Ghosh, Tianbao Yang:
Multi-Output Distributional Fairness via Post-Processing. - Dongyue Xie:
Empirical Bayes Trend Filtering Through a Variational Inference Framework. - Max Wasserman, Gonzalo Mateos:
Stabilizing the Kumaraswamy Distribution. - Inwon Kang, Parikshit Ram, Yi Zhou, Horst Samulowitz, Oshani Seneviratne:
On Learning Representations for Tabular Data Distillation. - Inkyu Shin, Qihang Yu, Xiaohui Shen, In So Kweon, Kuk-Jin Yoon, Liang-Chieh Chen:
Enhancing Temporal Consistency in Video Editing by Reconstructing Videos with 3D Gaussian Splatting. - Nisha Lakshmana Raichur, Lucas Heublein, Tobias Feigl, Alexander Rügamer, Christopher Mutschler, Felix Ott:
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning. - Aditya Hemant Shahane, Prathosh AP, Sandeep Kumar:
GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision. - Brian Godwin Lim, Galvin Brice Sy Lim, Renzo Roel Tan, Kazushi Ikeda:
Contextualized Messages Boost Graph Representations. - Aditya Challa, Sravan Danda, Laurent Najman, Snehanshu Saha:
Quantile Activation: Correcting a failure mode of traditional ML models. - Anh Quang Dang, Reza Babanezhad Harikandeh, Sharan Vaswani:
(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum. - Teresa Yeo, Andrei Atanov, Harold Benoit, Aleksandr Alekseev, Ruchira Ray, Pooya Esmaeil Akhoondi, Amir Zamir:
Controlled Training Data Generation with Diffusion Models. - Hoang Anh Dung, Cuong C. Nguyen, Vasileios Belagiannis, Thanh-Toan Do, Gustavo Carneiro:
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning. - Akiyoshi Sannai, Yasunari Hikima, Ken Kobayashi, Akinori Tanaka, Naoki Hamada:
Bézier Flow: a Surface-wise Gradient Descent Method for Multi-objective Optimization. - Ameesh Shah, Cameron Voloshin, Chenxi Yang, Abhinav Verma, Swarat Chaudhuri, Sanjit A. Seshia:
LTL-Constrained Policy Optimization with Cycle Experience Replay. - Francesco Di Salvo, Sebastian Doerrich, Ines Rieger, Christian Ledig:
An Embedding is Worth a Thousand Noisy Labels. - Arjhun Swaminathan, Mete Akgün:
Distributed and Secure Kernel-Based Quantum Machine Learning. - Shuhao Fu, Andrew Jun Lee, Anna Wang, Ida Momennejad, Trevor Bihl, Hongjing Lu, Taylor Whittington Webb:
Evaluating Compositional Scene Understanding in Multimodal Generative Models. - Karthik Valmeekam, Kaya Stechly, Atharva Gundawar, Subbarao Kambhampati:
A Systematic Evaluation of the Planning and Scheduling Abilities of the Reasoning Model o1. - Letian Fu, Long Lian, Renhao Wang, Baifeng Shi, Xudong Wang, Adam Yala, Trevor Darrell, Alexei A. Efros, Ken Goldberg:
Rethinking Patch Dependence for Masked Autoencoders. - Yueming Lyu, Xiaowei Zhou, Xingrui Yu, Ivor W. Tsang:
Graph Potential Field Neural Network for Massive Agents Group-wise Path Planning. - Xin Ma, Yaohui Wang, Xinyuan Chen, Gengyun Jia, Ziwei Liu, Yuan-Fang Li, Cunjian Chen, Yu Qiao:
Latte: Latent Diffusion Transformer for Video Generation. - Han Guo, Ramtin Hosseini, Ruiyi Zhang, Sai Ashish Somayajula, Ranak Roy Chowdhury, Rajesh K. Gupta, Pengtao Xie:
Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization. - Marc A. Tunnell, Zachary J. DeBruine, Erin Carrier:
Rank Suggestion in Non-negative Matrix Factorization: Residual Sensitivity to Initial Conditions (RSIC). - Daniele Bracale, Moulinath Banerjee, Yuekai Sun, Salam Turki, Kevin Stoll:
Dynamic Pricing in the Linear Valuation Model using Shape Constraints. - Lorenzo Basile, Valentino Maiorca, Luca Bortolussi, Emanuele Rodolà, Francesco Locatello:
ResiDual Transformer Alignment with Spectral Decomposition. - Kishan Gurumurthy, Himanshu Pal, Charu Sharma:
Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs. - Bao Duong, Hung Le, Biwei Huang, Thin Nguyen:
Reinforcement Learning for Causal Discovery without Acyclicity Constraints. - Clement Nyanhongo, Bruno Miranda Henrique, Eugene Santos:
Reward Distance Comparisons Under Transition Sparsity. - Anka Reuel, Benjamin Bucknall, Stephen Casper, Timothy Fist, Lisa Soder, Onni Aarne, Lewis Hammond, Lujain Ibrahim, Alan Chan, Peter Wills, Markus Anderljung, Ben Garfinkel, Lennart Heim, Andrew Trask, Gabriel Mukobi, Rylan Schaeffer, Mauricio Baker, Sara Hooker, Irene Solaiman, Sasha Luccioni, Nitarshan Rajkumar, Nicolas Moës, Jeffrey Ladish, David Bau, Paul Bricman, Neel Guha, Jessica Newman, Yoshua Bengio, Tobin South, Alex Pentland, Sanmi Koyejo, Mykel J. Kochenderfer, Robert Trager:
Open Problems in Technical AI Governance. - Alexander Robey, Eric Wong, Hamed Hassani, George J. Pappas:
SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks. - Diogo S. Carvalho, Pedro A. Santos, Francisco S. Melo:
Multi-Bellman operator for convergence of Q-learning with linear function approximation. - Arwin Gansekoele, Sandjai Bhulai, Mark Hoogendoorn, Rob van der Mei:
Relative Phase Equivariant Deep Neural Systems for Physical Layer Communications. - Haiqing Hao, Wenhui Wang:
Bayesian Transferability Assessment for Spiking Neural Networks. - Jakub Lucki, Boyi Wei, Yangsibo Huang, Peter Henderson, Florian Tramèr, Javier Rando:
An Adversarial Perspective on Machine Unlearning for AI Safety. - Tobias Ladner, Michael Eichelbeck, Matthias Althoff:
Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure. - Nabarun Goswami
, Hanqin Wang, Tatsuya Harada:
EDM-TTS: Efficient Dual-Stage Masked Modeling for Alignment-Free Text-to-Speech Synthesis. - Sahra Ghalebikesabi, Eugene Bagdasarian, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle:
Privacy Awareness for Information-Sharing Assistants: A Case-study on Form-filling with Contextual Integrity. - Yangyi Chen, Binxuan Huang, Yifan Gao, Zhengyang Wang, Jingfeng Yang, Heng Ji:
Scaling Laws for Predicting Downstream Performance in LLMs. - Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko, Jianfeng Zhang, Bo An:
Leveraging Gradients for Unsupervised Accuracy Estimation under Distribution Shift. - Paul-Hieu V. Nguyen, Ryan Yee, Sameer K. Deshpande:
Oblique Bayesian Additive Regression Trees. - Hongfei Wu, Lijun Wu, Guoqing Liu, Zhirong Liu, Bin Shao, Zun Wang:
SE3Set: Harnessing Equivariant Hypergraph Neural Networks for Molecular Representation Learning. - Makoto Takamoto, Daniel Oñoro-Rubio, Wiem Ben Rim, Takashi Maruyama, Bhushan Kotnis:
Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction. - Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig:
Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty. - Ali Modarressi, Abdullatif Köksal, Ayyoob Imani, Mohsen Fayyaz, Hinrich Schütze:
MemLLM: Finetuning LLMs to Use Explicit Read-Write Memory. - Matéo Mahaut, Roberto Dessì, Francesca Franzon, Marco Baroni:
Referential communication in heterogeneous communities of pre-trained visual deep networks. - Ye Yuan, Youyuan Zhang, Can Chen, Haolun Wu, Melody Zixuan Li, Jianmo Li, James J. Clark, Xue Liu:
Design Editing for Offline Model-based Optimization. - Edgar Torres, Mathias Niepert:
Adaptive Physics-informed Neural Networks: A Survey. - Yik Lun Kei, Jialiang Li, Hangjian Li, Yanzhen Chen, Oscar Hernan Madrid Padilla:
Change Point Detection in Dynamic Graphs with Decoder-only Latent Space Model. - Jose L. Garcia, Karolina Hajkova, Maria Marchenko, Carlos Miguel Patiño:
Reproducibility Study of "Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation". - Daniel P. Jeong, Zachary Chase Lipton, Pradeep Kumar Ravikumar:
LLM-Select: Feature Selection with Large Language Models. - Alexis Thual, Yohann Benchetrit, Felix Geilert, Jérémy Rapin, Iurii Makarov, Stanislas Dehaene, Bertrand Thirion, Hubert J. Banville, Jean-Remi King:
Sample-efficient decoding of visual stimuli from fMRI through inter-individual functional alignment. - Gobinda Saha, Kaushik Roy:
Amphibian: A Meta-Learning Framework for Rehearsal-Free, Fast Online Continual Learning. - Futoon M. Abushaqra, Hao Xue, Yongli Ren, Flora D. Salim:
ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting. - Tatyana Benko, Martin Buck, Ilya Amburg, Stephen J. Young, Sinan G. Aksoy:
HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network. - Tomoyuki Obuchi, Toshiyuki Tanaka:
When resampling/reweighting improves feature learning in imbalanced classification? A toy-model study. - Jiaxi Wang, Yaosen Min, Miao Li, Ji Wu:
FragFormer: A Fragment-based Representation Learning Framework for Molecular Property Prediction. - Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan Ö. Arik, Jinsung Yoon:
LLM-Guided Self-Supervised Tabular Learning With Task-Specific Pre-text Tasks. - Emmanouil Kariotakis, Nicholas D. Sidiropoulos, Aritra Konar:
Fairness-Aware Dense Subgraph Discovery. - Sofiane Ennadir, Gabriela Zarzar Gandler, Filip Cornell, Lele Cao, Oleg Smirnov, Tianze Wang, Levente Zólyomi, Björn Brinne, Sahar Asadi:
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review. - Jing Xiong, Gongye Liu, Lun Huang, Chengyue Wu, Taiqiang Wu, Yao Mu, Yuan Yao, Hui Shen, Zhongwei Wan, Jinfa Huang, Chaofan Tao, Shen Yan, Huaxiu Yao, Lingpeng Kong, Hongxia Yang, Mi Zhang, Guillermo Sapiro, Jiebo Luo, Ping Luo, Ngai Wong:
Autoregressive Models in Vision: A Survey. - Anuj Singh, Sayak Mukherjee, Ahmad Beirami, Hadi Jamali Rad:
CoDe: Blockwise Control for Denoising Diffusion Models. - Wenjian Hao, Devesh Upadhyay, Shaoshuai Mou:
Deep Koopman Learning using Noisy Data. - Alexander Kolesnikov, André Susano Pinto, Michael Tschannen:
Jet: A Modern Transformer-Based Normalizing Flow. - Nam Hyeon-Woo, Moon Ye-Bin, Wonseok Choi, Lee Hyun, Tae-Hyun Oh:
VLM's Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models. - Markus Lange-Hegermann, Christoph Zimmer:
Future-aware Safe Active Learning of Time Varying Systems using Gaussian Processes. - Ankur Nath, Alan Kuhnle:
MaxCutBench: Revisiting and Benchmarking Graph Neural Networks for Maximum Cut. - Prateek Yadav, Leshem Choshen, Colin Raffel, Mohit Bansal:
ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization. - Vignesh Kothapalli, Tom Tirer:
Can Kernel Methods Explain How the Data Affects Neural Collapse? - Sheng Yang, Peihan Liu, Cengiz Pehlevan:
Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space. - Ricardo Baptista, Michael Brennan, Youssef Marzouk:
Dimension reduction via score ratio matching. - Andres Fernandez, Frank Schneider, Maren Mahsereci, Philipp Hennig:
Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods. - Xingyuan Zhang, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl:
Overcoming Knowledge Barriers: Online Imitation Learning from Visual Observation with Pretrained World Models. - Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu:
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics. - Yedi Zhang, Andrew M. Saxe, Peter E. Latham:
When Are Bias-Free ReLU Networks Effectively Linear Networks? - Sungmin Cha, Kyunghyun Cho:
Hyperparameters in Continual Learning: A Reality Check. - Mingqi Yuan, Roger Creus Castanyer, Bo Li, Xin Jin, Wenjun Zeng, Glen Berseth:
RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning. - Jeremy Wohlwend, Mateo Reveiz, Matt McPartlon, Axel Feldmann, Wengong Jin, Regina Barzilay:
MiniFold: Simple, Fast, and Accurate Protein Structure Prediction. - Lazar Atanackovic, Emmanuel Bengio:
Investigating Generalization Behaviours of Generative Flow Networks. - Prateek Yadav, Colin Raffel, Mohammed Muqeeth, Lucas Caccia, Haokun Liu, Tianlong Chen, Mohit Bansal, Leshem Choshen, Alessandro Sordoni:
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning. - Haoyue Bai, Xuefeng Du, Katie Rainey, Shibin Parameswaran, Yixuan Li:
Out-of-Distribution Learning with Human Feedback. - Amitai Yacobi, Ofir Lindenbaum, Uri Shaham:
Generalizable and Robust Spectral Method for Multi-view Representation Learning. - Jiahe Lin, Yikai Zhang, George Michailidis:
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees. - Can Chen, Gabriel L. Oliveira, Hossein Sharifi-Noghabi, Tristan Sylvain:
LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling. - Akihiro Kubo, Paavo Parmas, Shin Ishii:
Double Horizon Model-Based Policy Optimization. - William St-Arnaud, Margarida Carvalho, Golnoosh Farnadi:
A Learning-Based Framework for Fair and Scalable Solution Generation in Kidney Exchange Problems. - Menghua Wu, Yujia Bao, Regina Barzilay, Tommi S. Jaakkola:
Sample, estimate, aggregate: A recipe for causal discovery foundation models. - Abrar Zahin, Rajasekhar Anguluri, Lalitha Sankar, Oliver Kosut, Gautam Dasarathy:
Robust Model Selection of Gaussian Graphical Models. - Haishan Wang, Arno Solin, Vikas K. Garg:
Heterophily-informed Message Passing. - Fang Chen, Gourav Datta, Mujahid Al Rafi, Hyeran Jeon, Meng Tang:
ReDistill: Residual Encoded Distillation for Peak Memory Reduction of CNNs. - Pierre Wolinski, Julyan Arbel:
Gaussian Pre-Activations in Neural Networks: Myth or Reality? - Peter Ochieng:
Speech Synthesis By Unrolling Diffusion Process using Neural Network Layers. - Yinhan He, Wendy Zheng, Yaochen Zhu, Jing Ma, Saumitra Mishra, Natraj Raman, Ninghao Liu, Jundong Li:
Global Graph Counterfactual Explanation: A Subgraph Mapping Approach. - Vahan Martirosyan, Jhony H. Giraldo, Fragkiskos D. Malliaros:
Piecewise Constant Spectral Graph Neural Network. - Payman Behnam, Uday Kamal, Sanjana Vijay Ganesh, Zhaoyi Li, Michael Jurado, Alind Khare, Igor Fedorov, Gaowen Liu, Alexey Tumanov:
∇QDARTS: Quantization as an Elastic Dimension to Differentiable NAS. - Bruno Després:
A functional framework for nonsmooth autodiff with maxpooling functions. - Aref Miri Rekavandi, Olga Ohrimenko, Benjamin I. P. Rubinstein:
RS-Reg: Probabilistic and Robust Certified Regression through Randomized Smoothing. - Yuting Tang, Xin-Qiang Cai, Yao-Xiang Ding, Qiyu Wu, Guoqing Liu, Masashi Sugiyama:
Reinforcement Learning from Bagged Reward. - Apurv Verma, Satyapriya Krishna, Sebastian Gehrmann, Madhavan Seshadri, Anu Pradhan, John A. Doucette, David Rabinowitz, Leslie Barrett, Tom Ault, Hai Phan:
Operationalizing a Threat Model for Red-Teaming Large Language Models (LLMs). - Chun Tao, Timur Ibrayev, Kaushik Roy:
Semantic-Syntactic Discrepancy in Images (SSDI): Learning Meaning and Order of Features from Natural Images. - Weiqin Chen, Santiago Paternain:
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons. - Lijie Hu, Tianhao Huang, Lu Yu, Wanyu Lin, Tianhang Zheng, Di Wang:
Faithful Interpretation for Graph Neural Networks. - Tiancheng Lao, Xudong Guo, Mengge Liu, Junjie Yu, Yi Liu, Wenhui Fan:
Efficient Exploration in Multi-Agent Reinforcement Learning via Farsighted Self-Direction. - Ivan Stelmakh, John Frederick Wieting, Yang Xi, Graham Neubig, Nihar B. Shah:
A Gold Standard Dataset for the Reviewer Assignment Problem. - Çagatay Yildiz, Nishaanth Kanna Ravichandran, Nitin Sharma, Matthias Bethge, Beyza Ermis:
Investigating Continual Pretraining in Large Language Models: Insights and Implications. - Syrine Belakaria, Alaleh Ahmadianshalchi, Barbara E. Engelhardt, Stefano Ermon, Jana Doppa:
Non-Myopic Multi-Objective Bayesian Optimization. - Anna Kuzina, Haotian Chen, Babak Esmaeili, Jakub M. Tomczak:
Variational Stochastic Gradient Descent for Deep Neural Networks. - Yen-Ru Lai, Fu-Chieh Chang, Pei-Yuan Wu:
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement Learning. - Izzeddin Teeti, Aniket Thomas, Munish Monga, Sachin Kumar Giroh, Uddeshya Singh, Andrew Bradley, Biplab Banerjee, Fabio Cuzzolin:
ASTRA: A Scene-aware Transformer-based Model for Trajectory Prediction. - Gourav Datta, Zeyu Liu, James Diffenderfer, Bhavya Kailkhura, Peter Anthony Beerel:
When SNN meets ANN: Error-Free ANN-to-SNN Conversion for Extreme Edge Efficiency. - Nathan Sun, Constantin-Daniel Nicolae, Sara Sameer, Karena Yan:
Optimizing Cycle Life Prediction of Lithium-ion Batteries via a Physics-Informed Model. - Nabarun Goswami, Yusuke Mukuta, Tatsuya Harada:
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space. - Zhouyang Liu, Ning Liu, Yixin Chen, Ziqing Wen, Jiezhong He, Dongsheng Li:
Graph Theory-Based Deep Graph Similarity Learning: A Unified Survey of Pipeline, Techniques, and Challenges. - Kartik Sharma, Vineeth Rakesh, Yingtong Dou, Srijan Kumar, Mahashweta Das:
Personalized Layer Selection for Graph Neural Networks. - Zhouliang Yu, Yuhuan Yuan, Tim Z. Xiao, Fuxiang Frank Xia, Jie Fu, Ge Zhang, Ge Lin, Weiyang Liu:
Generating Symbolic World Models via Test-time Scaling of Large Language Models. - Jongmin Lee, Amin Rakhsha, Ernest K. Ryu, Amir-massoud Farahmand:
Deflated Dynamics Value Iteration. - André Hottung, Paula Wong-Chung, Kevin Tierney:
Neural Deconstruction Search for Vehicle Routing Problems. - Xinzhe Li:
A Survey on LLM Test-Time Compute via Search: Tasks, LLM Profiling, Search Algorithms, and Relevant Frameworks. - Jixiang Qing, Rebecca D. Langdon, Robert M. Lee, Behrang Shafei, Mark van der Wilk, Calvin Tsay, Ruth Misener:
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization. - Soroush Abbasi Koohpayegani, Anuj Singh, Navaneet K. L., Hamed Pirsiavash, Hadi Jamali Rad:
GeNIe: Generative Hard Negative Images Through Diffusion. - Salma Abdel Magid, Weiwei Pan, Simon Warchol, Grace Guo, Junsik Kim, Mahia Rahman, Hanspeter Pfister:
Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models. - Zhou Wang, Xingye Qiao:
Generalized Prediction Set with Bandit Feedback. - Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Mengyuan Li, Kaize Ding, Yue Zhao:
LEGO-Learn: Label-Efficient Graph Open-Set Learning. - Robin Ghyselinck, Valentin Delchevalerie, Bruno Dumas, Benoît Frénay:
On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation. - Tyme Chatupanyachotikul, Leonard Horns, Matei Nastase:
[RE] GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries. - Naganand Yadati:
LocalFormer: Mitigating Over-Globalising in Transformers on Graphs with Localised Training. - Carl R. Richardson, Matthew C. Turner, Steve R. Gunn:
Lurie Networks with Robust Convergent Dynamics. - Li Guo, George Andriopoulos, Zifan Zhao, Zixuan Dong, Shuyang Ling, Keith W. Ross:
Cross Entropy versus Label Smoothing: A Neural Collapse Perspective. - Oliver Schulte, Pascal Poupart:
When Should Reinforcement Learning Use Causal Reasoning? - Bhishma Dedhia, Niraj K. Jha:
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations. - Domonkos Nagy, Lohithsai Yadala Chanchu, Krystof Bobek, Xin Zhou, Jacobus Smit:
Remembering to Be Fair Again: Reproducing Non-Markovian Fairness in Sequential Decision Making. - Junn Yong Loo, Fang Yu Leong, Michelle Adeline, Julia Kaiwen Lau, Hwa Hui Tew, Arghya Pal, Vishnu Monn Baskaran, Chee-Ming Ting, Raphaël C.-W. Phan:
Learning Energy-Based Generative Models via Potential Flow: A Variational Principle Approach to Probability Density Homotopy Matching. - Xiachong Feng, Longxu Dou, Minzhi Li, Qinghao Wang, Yu Guo, Haochuan Wang, Chang Ma, Lingpeng Kong:
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios. - Zhenhan Huang, Kavitha Srinivas, Horst Samulowitz, Niharika S. D'Souza, Charu C. Aggarwal, Pin-Yu Chen, Jianxi Gao:
Language Models Are Good Tabular Learners. - Gouki Minegishi, Yusuke Iwasawa, Yutaka Matsuo:
Bridging Lottery Ticket and Grokking: Understanding Grokking from Inner Structure of Networks. - Manogna Sreenivas, Soma Biswas:
Efficient Open Set Single Image Test Time Adaptation of Vision Language Models. - Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai:
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach. - Aniq Ur Rahman, Justin P. Coon:
Node Feature Forecasting in Temporal Graphs: an Interpretable Online Algorithm. - Ge Ya Luo, Zhi Hao Luo, Anthony Gosselin, Alexia Jolicoeur-Martineau, Christopher Pal:
Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion. - Shahzad Ahmad, Sukalpa Chanda, Yogesh S. Rawat:
T2L: Efficient Zero-Shot Action Recognition with Temporal Token Learning. - Yuan Pu, Yazhe Niu, Zhenjie Yang, Jiyuan Ren, Hongsheng Li, Yu Liu:
UniZero: Generalized and Efficient Planning with Scalable Latent World Models. - Jiachen Yao, Lingjie Yi, Mayank Goswami, Chao Chen:
A Theoretical Study of Neural Network Expressive Power via Manifold Topology. - Shang Liu, Zhongze Cai, Guanting Chen, Xiaocheng Li:
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification. - Ling-Qi Zhang, Zahra Kadkhodaie, Eero P. Simoncelli, David H. Brainard:
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models. - Kuangyu Ding, Nachuan Xiao, Kim-Chuan Toh:
Adam-family Methods with Decoupled Weight Decay in Deep Learning. - Raul Astudillo, Kejun Li, Maegan Tucker, Chu Xin Cheng, Aaron D. Ames, Yisong Yue:
Preferential Multi-Objective Bayesian Optimization. - Yukun Li, Sijia Wang, Lifu Huang, Liping Liu:
Graph-based Confidence Calibration for Large Language Models. - Saumyaranjan Mohanty, Chimata Anudeep, Konda Reddy Mopuri:
Noise-free Loss Gradients: A Surprisingly Effective Baseline for Coreset Selection. - Liqiang Jing, Xinya Du:
FGAIF: Aligning Large Vision-Language Models with Fine-grained AI Feedback. - Nahush Lele, Arnav Chavan, Aryamaan Thakur, Deepak K. Gupta:
Rethinking the Value of Training-Free Structured Pruning of LLMs. - Udita Ghosh, Dripta S. Raychaudhuri, Jiachen Li, Konstantinos Karydis, Amit Roy-Chowdhury:
Robust Offline Imitation Learning from Diverse Auxiliary Data. - Hiroki Naganuma, Ryuichiro Hataya, Kotaro Yoshida, Ioannis Mitliagkas:
An Empirical Study of Pre-trained Model Selection for Out-of-Distribution Generalization and Calibration. - Amin Heyrani Nobari, Lyle Regenwetter, Giorgio Giannone, Faez Ahmed:
NITO: Neural Implicit Fields for Resolution-free and Domain-Adaptable Topology Optimization. - Mahdi Beitollahi, Alex Bie, Sobhan Hemati, Leo Maxime Brunswic, Xu Li, Xi Chen, Guojun Zhang:
Foundation Models Meet Federated Learning: A One-shot Feature-sharing Method with Privacy and Performance Guarantees. - Riccardo Cappuzzo, Aimee Coelho, Félix Lefebvre, Paolo Papotti, Gaël Varoquaux:
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes. - Sourya Basu, Suhas Lohit, Matthew Brand:
G-RepsNet: A Lightweight Construction of Equivariant Networks for Arbitrary Matrix Groups. - Jeffrey Wen, Rizwan Ahmad, Philip Schniter:
Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems. - Alan Chan, Kevin Wei, Sihao Huang, Nitarshan Rajkumar, Elija Perrier, Seth Lazar, Gillian K. Hadfield, Markus Anderljung:
Infrastructure for AI Agents. - Ruijie Jiang, Thuan Nguyen, Shuchin Aeron, Prakash Ishwar:
Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-Collapse. - Yang Zhang, Chenjia Bai, Bin Zhao, Junchi Yan, Xiu Li, Xuelong Li:
Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models. - Uday Bhaskar Kuchipudi, Jayadratha Gayen, Charu Sharma, Naresh Manwani:
Node Classification With Reject Option. - Matthieu Jonckheere, Chiara Mignacco, Gilles Stoltz:
Policy Optimization via Adv2: Adversarial Learning on Advantage Functions. - Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon:
Closed-Form Diffusion Models. - Jeremiah Birrell:
Statistical Error Bounds for GANs with Nonlinear Objective Functionals. - Camila Kolling, Till Speicher, Vedant Nanda, Mariya Toneva, Krishna P. Gummadi:
Investigating the Effects of Fairness Interventions Using Pointwise Representational Similarity. - Haoyang Li, Yiming Li, Anxin Tian, Tianhao Tang, Zhanchao Xu, Xuejia Chen, Nicole Hu, Wei Dong, Qing Li, Lei Chen:
A Survey on Large Language Model Acceleration based on KV Cache Management. - Numair Sani, Daniel Malinsky, Ilya Shpitser:
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning. - Charles K. Assaad:
Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion. - Zifeng Ding, Yifeng Li, Yuan He, Antonio Norelli, Jingcheng Wu, Volker Tresp, Michael M. Bronstein, Yunpu Ma:
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models. - Jun Han, Zixiang Chen, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu:
Guided Discrete Diffusion for Electronic Health Record Generation. - Raja Kumar, Raghav Singhal, Pranamya Prashant Kulkarni, Deval Mehta, Kshitij Sharad Jadhav:
M3CoL: Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification. - Karim Kassab, Antoine Schnepf, Jean-Yves Franceschi, Laurent Caraffa, Jérémie Mary, Valérie Gouet-Brunet:
RefinedFields: Radiance Fields Refinement for Planar Scene Representations. - Pengcheng Xu, Li Yi, Gezheng Xu, Xi Chen, A. Ian McLeod, Charles Ling, Boyu Wang:
Uniform Noise Distribution and Compact Clusters: Unveiling the Success of Self-Supervised Learning in Label Noise. - Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An Thai Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles D. Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffmann:
Machine Learning with Physics Knowledge for Prediction: A Survey. - Antoine Siraudin, Fragkiskos D. Malliaros, Christopher Morris:
Cometh: A continuous-time discrete-state graph diffusion model. - Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Geoff Holmes:
Pruning Feature Extractor Stacking for Cross-domain Few-shot Learning. - Ziqing Xu, Hancheng Min, Salma Tarmoun, Enrique Mallada, René Vidal:
A Local Polyak-Łojasiewicz and Descent Lemma of Gradient Descent For Overparametrized Linear Models. - Davide Carbone:
Hitchhiker's guide on the relation of Energy-Based Models with other generative models, sampling and statistical physics: a comprehensive review. - Priya Kasimbeg, Vincent Roulet, Naman Agarwal, Sourabh Medapati, Fabian Pedregosa, Atish Agarwala, George E. Dahl:
How far away are truly hyperparameter-free learning algorithms? - Ben Chugg, Hongjian Wang, Aaditya Ramdas:
Time-Uniform Confidence Spheres for Means of Random Vectors. - Gaurav Chaudhary, Washim Uddin Mondal, Laxmidhar Behera:
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning. - Weigao Sun, Zhen Qin, Dong Li, Xuyang Shen, Yu Qiao, Yiran Zhong:
LASP: Linear Attention Sequence Parallelism. - Reabetswe M. Nkhumise, Debabrota Basu, Tony J. Prescott, Aditya Gilra:
Studying Exploration in RL: An Optimal Transport Analysis of Occupancy Measure Trajectories. - Wei-Hsiang Liao, Yuhta Takida, Yukara Ikemiya, Zhi Zhong, Chieh-Hsin Lai, Giorgio Fabbro, Kazuki Shimada, Keisuke Toyama, Kin Wai Cheuk, Marco A. Martínez Ramírez, Shusuke Takahashi, Stefan Uhlich, Taketo Akama, Woosung Choi, Yuichiro Koyama, Yuki Mitsufuji:
Music Foundation Model as Generic Booster for Music Downstream Tasks. - Ehsan Futuhi, Shayan Karimi, Chao Gao, Martin Müller:
ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control. - Felix Divo, Eric Endress, Kevin Endler, Kristian Kersting, Devendra Singh Dhami:
Forecasting Company Fundamentals. - Rickard Brüel Gabrielsson, Tongzhou Wang, Manel Baradad, Justin Solomon:
Deep Augmentation: Dropout as Augmentation for Self-Supervised Learning. - Jinhao Li, Sarah Monazam Erfani, Lei Feng, James Bailey, Feng Liu:
Exploring Weak-to-Strong Generalization for CLIP-based Classification. - Zheyuan Zhan, Defang Chen, Jian-Ping Mei, Zhenghe Zhao, Jiawei Chen, Chun Chen, Siwei Lyu, Can Wang:
Conditional Image Synthesis with Diffusion Models: A Survey. - Huzaifa Arif, Pin-Yu Chen, Keerthiram Murugesan, Alex Gittens:
Group Fair Federated Learning via Stochastic Kernel Regularization. - Priscilla Ong, Manuel Haussmann, Otto Lönnroth, Harri Lähdesmäki:
Latent mixed-effect models for high-dimensional longitudinal data. - Md. Ibrahim Ibne Alam, Parikshit Ram, Soham Dan, Horst Samulowitz, Koushik Kar:
On the Utility of Existing Fine-Tuned Models on Data-Scarce Domains. - Chenhui Zhao, Liyue Shen:
Part-aware Prompted Segment Anything Model for Adaptive Segmentation. - Praveen Srinivasa Varadhan, Amogh Gulati, Ashwin Sankar, Srija Anand, Anirudh Gupta, Anirudh Mukherjee, Shiva Kumar Marepally, Ankur Bhatia, Saloni Jaju, Suvrat Bhooshan, Mitesh M. Khapra:
Rethinking MUSHRA: Addressing Modern Challenges in Text-to-Speech Evaluation. - James Y. Huang, Wenxuan Zhou, Fei Wang, Fred Morstatter, Sheng Zhang, Hoifung Poon, Muhao Chen:
Offset Unlearning for Large Language Models. - Pranav Maneriker, Aditya T. Vadlamani, Anutam Srinivasan, Yuntian He, Ali Payani, Srinivasan Parthasarathy:
Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs. - Zhuoqun Chen, Xiu Yuan, Tongzhou Mu, Hao Su:
Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient Visuomotor Control. - Haozhe Liu, Shikun Liu, Zijian Zhou, Mengmeng Xu, Yanping Xie, Xiao Han, Juan Camilo Pérez, Ding Liu, Kumara Kahatapitiya, Menglin Jia, Jui-Chieh Wu, Sen He, Tao Xiang, Jürgen Schmidhuber, Juan-Manuel Pérez-Rúa:
MarDini: Masked Auto-regressive Diffusion for Video Generation at Scale. - Keivan Rezaei, Khyathi Raghavi Chandu, Soheil Feizi, Yejin Choi, Faeze Brahman, Abhilasha Ravichander:
RESTOR: Knowledge Recovery in Machine Unlearning. - Hui Shen, Jingxuan Zhang, Boning Xiong, Rui Hu, Shoufa Chen, Zhongwei Wan, Xin Wang, Yu Zhang, Zixuan Gong, Guangyin Bao, Chaofan Tao, Yongfeng Huang, Ye Yuan, Mi Zhang:
Efficient Diffusion Models: A Survey. - Thang D. Bui, Matthew Ashman, Richard E. Turner:
Tighter sparse variational Gaussian processes. - Jihun Kim, Javad Lavaei:
Online Bandit Nonlinear Control with Dynamic Batch Length and Adaptive Learning Rate. - Monika Wysoczanska, Antonín Vobecký, Amaia Cardiel, Tomasz Trzcinski, Renaud Marlet, Andrei Bursuc, Oriane Siméoni:
Test-time Contrastive Concepts for Open-world Semantic Segmentation with Vision-Language Models. - Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aïssa-El-Bey:
Augmented Invertible Koopman Autoencoder for long-term time series forecasting. - Youssef Mroueh, Apoorva Nitsure:
Information Theoretic Guarantees For Policy Alignment In Large Language Models. - Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy:
Generalizable Representation Learning for fMRI-based Neurological Disorder Identification. - Maria-Florina Balcan, Anh Tuan Nguyen, Dravyansh Sharma:
Algorithm Configuration for Structured Pfaffian Settings. - Eduardo Fernandes Montesuma, Adel El Habazi, Fred Maurice Ngolè Mboula:
Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport. - Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang, Manling Li, Jiajun Wu, Heng Ji:
Visually Descriptive Language Model for Vector Graphics Reasoning. - Andreas Kirsch:
(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models. - Sayan Mukherjee, Vorapong Suppakitpaisarn:
Local Differential Privacy-Preserving Spectral Clustering for General Graphs. - Taraneh Younesian, Daniel Daza, Emile van Krieken, Thiviyan Thanapalasingam, Peter Bloem:
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks. - Ben Anson, Edward Milsom, Laurence Aitchison:
Flexible Infinite-Width Graph Convolutional Neural Networks. - Zehao Wang, Han Zhou, Matthew B. Blaschko, Tinne Tuytelaars, Minye Wu:
Diversity-Driven View Subset Selection for Indoor Novel View Synthesis. - Zohair Shafi, Ayan Chatterjee, Tina Eliassi-Rad:
Explaining Node Embeddings. - Lorenzo Dall'Amico, Enrico Maria Belliardo:
Learning distributed representations with efficient SoftMax normalization. - Keziah Naggita, Matthew R. Walter, Avrim Blum:
Learning Actionable Counterfactual Explanations in Large State Spaces. - Bernard Spiegl, Andrea Perin, Stéphane Deny, Alexander Ilin:
ViewFusion: Learning Composable Diffusion Models for Novel View Synthesis. - Hsiu-Hsuan Wang, Tan-Ha Mai, Nai-Xuan Ye, Wei-I Lin, Hsuan-Tien Lin:
CLImage: Human-Annotated Datasets for Complementary-Label Learning. - Yannick Assogba, Donghao Ren:
Evaluating Long Range Dependency Handling in Code Generation LLMs. - Mohammad Reza Rezaei, Adji Bousso Dieng:
Alternators For Sequence Modeling. - Manuel Dileo, Matteo Zignani, Sabrina Gaito:
Evaluating explainability techniques on discrete-time graph neural networks. - Caleb Cranney, Jesse G. Meyer:
AttentionSmithy: A Modular Framework for Rapid Transformer Development. - Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman, Ronan Le Bras, Ximing Lu, Nouha Dziri, Yejin Choi, Mark O. Riedl, Maarten Sap:
Multi-Attribute Constraint Satisfaction via Language Model Rewriting. - Siddhant Bhambri, Mudit Verma, Subbarao Kambhampati:
Do Think Tags Really Help LLMs Plan? A Critical Evaluation of ReAct-Style Prompting. - Guangyao Zhou, Sivaramakrishnan Swaminathan, Rajkumar Vasudeva Raju, J. Swaroop Guntupalli, Wolfgang Lehrach, Joseph Ortiz, Antoine Dedieu, Miguel Lázaro-Gredilla, Kevin Patrick Murphy:
Diffusion Model Predictive Control. - Liangliang Zhang, Haoran Bao, Yao Ma:
Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study. - Po-Yi Lu, Yi-Jie Cheng, Chun-Liang Li, Hsuan-Tien Lin:
An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets. - Zhehao Zhang, Ryan A. Rossi, Branislav Kveton, Yijia Shao, Diyi Yang, Hamed Zamani, Franck Dernoncourt, Joe Barrow, Tong Yu, Sungchul Kim, Ruiyi Zhang, Jiuxiang Gu, Tyler Derr, Hongjie Chen, Junda Wu, Xiang Chen, Zichao Wang, Subrata Mitra, Nedim Lipka, Nesreen K. Ahmed, Yu Wang:
Personalization of Large Language Models: A Survey. - Reda Bensaid, Vincent Gripon, François Leduc-Primeau, Lukas Mauch, Ghouthi Boukli Hacene, Fabien Cardinaux:
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models. - Hongkai Zheng, Wenda Chu, Austin Wang, Nikola Borislavov Kovachki, Ricardo Baptista, Yisong Yue:
Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems. - Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman:
To Be Greedy, or Not to Be - That Is the Question for Population Based Training Variants. - Sébastien Foulle:
Mathematical Characterization of Better-than-Random Multiclass Models. - Ju-Seung Byun, Andrew Perrault:
Normality-Guided Distributional Reinforcement Learning for Continuous Control. - Wenhan Gao, Jian Luo, Ruichen Xu, Yi Liu:
Dynamic Schwartz-Fourier Neural Operator for Enhanced Expressive Power. - Yixiang Yao, Weizhao Jin, Srivatsan Ravi:
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution. - Mengzhao Jia, Wenhao Yu, Kaixin Ma, Tianqing Fang, Zhihan Zhang, Siru Ouyang, Hongming Zhang, Dong Yu, Meng Jiang:
Leopard: A Vision Language Model for Text-Rich Multi- Image Tasks. - Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito, Khanh Nguyen, Raouf Kerkouche, Kangsoo Jung, Joonas Jälkö, Lei Kang, Andrey Barsky, Vincent Poulain D'Andecy, Aurélie Joseph, Aashiq Muhamed, Kevin Kuo, Virginia Smith, Yusuke Yamasaki, Takumi Fukami, Kenta Niwa, Iifan Tyou, Hiro Ishii, Rio Yokota, Ragul N, Rintu Kutum, Josep Lladós, Ernest Valveny, Antti Honkela, Mario Fritz, Dimosthenis Karatzas:
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA. - Chunsan Hong, Tae-Hyun Oh, Minhyuk Sung:
MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models. - Kai Yi, Laurent Condat, Peter Richtárik:
Explicit Personalization and Local Training: Double Communication Acceleration in Federated Learning. - Lola Le Breton, Quentin Fournier, John Xavier Morris, Mariam El Mezouar, Sarath Chandar:
NeoBERT: A Next Generation BERT. - Nicholas Matthew Boffi, Michael Samuel Albergo, Eric Vanden-Eijnden:
Flow map matching with stochastic interpolants: A mathematical framework for consistency models. - Jared Fernandez, Luca Wehrstedt, Leonid Shamis, Mostafa Elhoushi, Kalyan Saladi, Yonatan Bisk, Emma Strubell, Jacob Kahn:
Efficient Hardware Scaling and Diminishing Returns in Large-Scale Training of Language Models. - Mete Kemertas, Allan Douglas Jepson, Amir-massoud Farahmand:
Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients. - Daniel Jarne Ornia, Giannis Delimpaltadakis, Jens Kober, Javier Alonso-Mora:
Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization. - Xingyue Huang, Miguel A. Romero Orth, Pablo Barceló, Michael M. Bronstein, Ismail Ilkan Ceylan:
Link Prediction with Relational Hypergraphs. - Zhexiao Xiong, Xin Xing, Scott Workman, Subash Khanal, Nathan Jacobs:
Mixed-View Panorama Synthesis using Geospatially Guided Diffusion. - Ismail Nejjar, Hao Dong, Olga Fink:
Recall and Refine: A Simple but Effective Source-free Open- set Domain Adaptation Framework. - Akifumi Yamada, Tomohiro Shiraishi, Shuichi Nishino, Teruyuki Katsuoka, Kouichi Taji, Ichiro Takeuchi:
Change Point Detection in the Frequency Domain with Statistical Reliability. - David Chen, Xinwei Li, Eui-Jin Kim, Prateek Bansal, David J. Nott:
Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making. - Aritra Bandyopadhyay, Chiranjeev Bindra, Roan van Blanken, Arijit Ghosh:
Reproducibility Study of 'SLICE: Stabilized LIME for Consistent Explanations for Image Classification'. - Aditya Somasundaram, Pushkal Mishra, Ayon Borthakur:
Learning Using a Single Forward Pass. - Ioannis Athanasiadis, Fredrik Lindsten, Michael Felsberg:
Prior Learning in Introspective VAEs. - Ciwan Ceylan, Kambiz Ghoorchian, Danica Kragic:
Full-Rank Unsupervised Node Embeddings for Directed Graphs via Message Aggregation. - Mohammed Baharoon, Jonathan Klein, Dominik L. Michels:
Harmony: A Joint Self-Supervised and Weakly-Supervised Framework for Learning General Purpose Visual Representations. - Adrian Hill, Guillaume Dalle:
Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiation. - Bum Jun Kim, Yoshinobu Kawahara, Sang Woo Kim:
Disappearance of Timestep Embedding: A Case Study on Neural ODE and Diffusion Models. - Leitian Tao, Xiang Chen, Tong Yu, Tung Mai, Ryan A. Rossi, Yixuan Li, Saayan Mitra:
CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement. - Xiaoyu Jiang, Sokratia Georgaka, Magnus Rattray, Mauricio A. Álvarez:
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference. - Shubhankar Agarwal, Hamzah Khan, Sandeep P. Chinchali, David Fridovich-Keil:
A Framework for Finding Local Saddle Points in Two-Player Zero-Sum Black-Box Games. - Xuelian Jiang, Tongtian Zhu, Yingxiang Xu, Can Wang, Yeyu Zhang, Fengxiang He:
Lie Symmetry Net: Preserving Conservation Laws in Modelling Financial Market Dynamics via Differential Equations. - Yu Sun, Vijja Wichitwechkarn, Ronald Clark, Mirko Kovac, Basaran Bahadir Kocer:
Metamorphic Forward Adaptation Network: Dynamically Adaptive and Modular Multi-layer Learning. - Giacomo Spigler:
Proximal Policy Distillation. - Michael J. Zellinger, Matt Thomson:
Rational Tuning of LLM Cascades via Probabilistic Modeling. - Abulikemu Abuduweili, Chenyang Yuan, Changliu Liu, Frank Permenter:
Enhancing Sample Generation of Diffusion Models using Noise Level Correction. - Izabela Kurek, Wojciech Trejter, Stipe Frkovic, Andro Erdelez:
[Re] Improving Interpretation Faithfulness for Vision Transformers. - Anirudhan Badrinath, Prabhat Agarwal, Jiajing Xu:
Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier. - Tsunehiko Tanaka, Kenshi Abe, Kaito Ariu, Tetsuro Morimura, Edgar Simo-Serra:
Return-Aligned Decision Transformer. - Jan Henrik Bertrand, Lukas Bierling, Ina Klaric, Aron Wezenberg:
[RE] GNNBoundary: Finding Boundaries and Going Beyond Them. - Zhihao Liu, Xianliang Yang, Zichuan Liu, Yifan Xia, Wei Jiang, Yuanyu Zhang, Lijuan Li, Guoliang Fan, Lei Song, Jiang Bian:
Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning. - Shivanshu Shekhar, Shreyas Singh, Tong Zhang:
SEE-DPO: Self Entropy Enhanced Direct Preference Optimization. - Simon Schrodi, Julian Schur, Max Argus, Thomas Brox:
Selective Concept Bottleneck Models Without Predefined Concepts. - Amine El Hattami, Nicolas Chapados, Christopher Pal:
Spaced Scheduling for Large Language Model Training. - Senmiao Wang, Yupeng Chen, Yushun Zhang, Ruoyu Sun, Tian Ding:
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective. - Inbal Rachel Livni Navon, Omer Reingold, Judy Hanwen Shen:
Fairness with respect to Stereotype Predictors: Impossibilities and Best Practices. - Ian Davidson, Nicolás Kennedy, S. S. Ravi:
CXAD: Contrastive Explanations for Anomaly Detection: Algorithms, Complexity Results and Experiments. - Leander Weber, Jim Berend, Moritz Weckbecker, Alexander Binder, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation. - Ziyan Wang, Yali Du, Yudi Zhang, Meng Fang, Biwei Huang:
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment. - Supriya Manna, Niladri Sett:
Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry. - Arto Maranjyan, Mher Safaryan, Peter Richtárik:
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity. - Chaouki Ben Issaid, Praneeth Vepakomma, Mehdi Bennis:
Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach. - Wenhao Li, Yudong Xu, Scott Sanner, Elias Boutros Khalil:
Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objects. - Antoine Godichon-Baggioni, Pierre Tarrago:
Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications. - Arash Tavakoli, Sina Ghiassian, Nemanja Rakicevic:
Learning in complex action spaces without policy gradients. - Charles A. Hepburn, Yue Jin, Giovanni Montana:
State-Constrained Offline Reinforcement Learning. - Kyoichi Iwasaki, Hideitsu Hino:
Dynamics of the accelerated t-SNE. - Yeruru Asrar Ahmed, Anurag Mittal:
End-to-end Training for Text-to-Image Synthesis using Dual-Text Embeddings. - Freek Byrman, Emma Kasteleyn, Bart Kuipers, Daniel Uyterlinde:
Revisiting Discover-then-Name Concept Bottleneck Models: A Reproducibility Study. - Chaoyun Zhang, Shilin He, Jiaxu Qian, Bowen Li, Liqun Li, Si Qin, Yu Kang, Minghua Ma, Guyue Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang:
Large Language Model-Brained GUI Agents: A Survey. - Sabine Muzellec, Andrea Alamia, Thomas Serre, Rufin VanRullen:
Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics. - Ruiyi Zhang, Sai Ashish Somayajula, Pengtao Xie:
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining. - Jayanta Dey, Haoyin Xu, Ashwin De Silva, Joshua T. Vogelstein:
Simple Calibration via Geodesic Kernels. - Navish Kumar, Thomas Möllenhoff, Mohammad Emtiyaz Khan, Aurélien Lucchi:
Optimization Guarantees for Square-Root Natural-Gradient Variational Inference. - Michael M. Jerge, David Evans:
Pitfalls in Evaluating Inference-time Methods for Improving LLM Reliability. - Sofía Pérez Casulo, Marcelo Fiori, Federico Larroca, Gonzalo Mateos:
LASE: Learned Adjacency Spectral Embeddings. - Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, Arvind Narayanan:
AI Agents That Matter. - Seyed Mahdi B. Azad, Zahra Padar, Gabriel Kalweit, Joschka Boedecker:
SR-Reward: Taking The Path More Traveled. - Ryan Chen, Ziteng Pang, Bradly C. Stadie:
Thoughts and Lessons on Using Visual Foundation Models for Manipulation. - Tomonari Tanaka, Hiroyuki Hanada, Hanting Yang, Tatsuya Aoyama, Yu Inatsu, Satoshi Akahane, Yoshito Okura, Noriaki Hashimoto, Taro Murayama, Hanju Lee, Shinya Kojima, Ichiro Takeuchi:
Distributionally Robust Coreset Selection under Covariate Shift. - Linfeng Ye, Shayan Mohajer Hamidi, En-Hui Yang:
Towards Undistillable Models by Minimizing Conditional Mutual Information. - Weizhe Chen, Sven Koenig, Bistra Dilkina:
Solving Multi-agent Path Finding as an LLM Benchmark: How, How Good and Why. - Shubham Ugare, Tarun Suresh, Hangoo Kang, Sasa Misailovic, Gagandeep Singh:
SynCode: LLM Generation with Grammar Augmentation. - Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar, Stephan Günnemann:
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks. - Sander Honig, Elyanne Oey, Lisanne Wallaard, Sharanda Suttorp, Clara Rus:
A reproducibility study of "User-item fairness tradeoffs in recommendations". - Hao Zhang, Di Chang, Fang Li, Mohammad Soleymani, Narendra Ahuja:
MagicPose4D: Crafting Articulated Models with Appearance and Motion Control. - Catalin E. Brita, Hieu Nguyen, Lubov Chalakova, Nikola Petrov:
Revisiting XRec: How Collaborative Signals Influence LLM-Based Recommendation Explanations. - Jorge Carrasco Pollo, Ioannis Kapetangeorgis, Joshua Rosenthal, John Hua Yao:
[Re] Benchmarking LLM Capabilities in Negotiation through Scoreable Games. - Aleksei Korneev, Jan Ramon:
A Survey on Verifiable Cross-Silo Federated Learning. - Ruben Figge, Sjoerd Gunneweg, Aaron Kuin, Mees Lindeman:
Reassessing Fairness: A Reproducibility Study of NIFA's Impact on GNN Models. - Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall, Wenchao Ma, Le Chen, Danhang Tang, Ulrich Neumann:
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation. - Atsuyuki Miyai, Jingkang Yang, Jingyang Zhang, Yifei Ming, Yueqian Lin, Qing Yu, Go Irie, Shafiq Joty, Yixuan Li, Hai Helen Li, Ziwei Liu, Toshihiko Yamasaki, Kiyoharu Aizawa:
Generalized Out-of-Distribution Detection and Beyond in Vision Language Model Era: A Survey. - Juheon Lee, Xiaohao Cai, Carola-Bibiane Schönlieb, Simon Masnou:
Neural varifolds: an aggregate representation for quantifying the geometry of point clouds. - Razan Baltaji, Saurabh Pujar, Martin Hirzel, Louis Mandel, Luca Buratti, Lav R. Varshney:
Cross-lingual Transfer in Programming Languages: An Extensive Empirical Study. - Zhuo Li, He Zhao, Jinke Ren, Anningzhe Gao, Dandan Guo, Xiang Wan, Hongyuan Zha:
Synthesizing Minority Samples for Long-tailed Classification via Distribution Matching. - Önder Akacik, Mark Hoogendoorn:
ModernTCN Revisited: A Critical Look at the Experimental Setup in General Time Series Analysis. - Siwei Yang, Bingchen Zhao, Cihang Xie:
AQA-Bench: An Interactive Benchmark for Evaluating LLMs' Sequential Reasoning Ability in Algorithmic Environments. - Youngseog Chung, Dhruv Malik, Jeff Schneider, Yuanzhi Li, Aarti Singh:
Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts. - Xiaozhuang Song, Yuzhao Tu, Tianshu Yu:
Enhancing Molecular Conformer Generation via Fragment- Augmented Diffusion Pretraining. - Matteo Tucat, Anirbit Mukherjee, Procheta Sen, Mingfei Sun, Omar Rivasplata:
Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks. - Furkan Mumcu, Yasin Yilmaz:
Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers. - Atharv Mittal, Agam Pandey, Amritanshu Tiwari, Sukrit Jindal, Swadesh Swain:
Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models. - Yiqing Liang, Mikhail Okunev, Mikaela Angelina Uy, Runfeng Li, Leonidas J. Guibas, James Tompkin, Adam W. Harley:
Monocular Dynamic Gaussian Splatting: Fast, Brittle, and Scene Complexity Rules. - Asen Dotsinski, Udit Thakur, Marko Ivanov, Mohammad Hafeez Khan, Maria Heuss:
On the Generalizability of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals". - Meher Changlani, Benjamin Hucko, Ioannis Kechagias, Aswin Krishna Mahadevan:
Reproducibility Study of "Improving Interpretation Faithfulness For Vision Transformers". - Jiawei Sun, Hongkang Li, Meng Wang:
Theoretical Learning Performance of Graph Networks: the Impact of Jumping Connections and Layer-wise Sparsification. - Divya Anand Sinha, Yezi Liu, Ruijie Du, Athina Markopoulou, Yanning Shen:
Gradient Inversion Attack on Graph Neural Networks. - Yangyang Shu, Yuhang Liu, Xiaofeng Cao, Qi Chen, Bowen Zhang, Ziqin Zhou, Anton van den Hengel, Lingqiao Liu:
Seeing Beyond Labels: Source-Free Domain Adaptation via Hypothesis Consolidation of Prediction Rationale. - Alice V. De Lorenci, Seung Eun Yi, Théo Moutakanni, Piotr Bojanowski, Camille Couprie, Juan C. Caicedo, Wolfgang Maximilian Anton Pernice:
Scaling Channel-Adaptive Self-Supervised Learning. - Sara Ghazanfari, Alexandre Araujo, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami:
EMMA: Efficient Visual Alignment in Multi-Modal LLMs. - Marco Bagatella, Andreas Krause, Georg Martius:
Directed Exploration in Reinforcement Learning from Linear Temporal Logic. - Ryoma Sato, Shinji Ito:
Influential Bandits: Pulling an Arm May Change the Environment. - Hanning Zhang, Pengcheng Wang, Shizhe Diao, Yong Lin, Rui Pan, Hanze Dong, Dylan Zhang, Pavlo Molchanov, Tong Zhang:
Entropy-Regularized Process Reward Model. - Andrii Skliar, Ties van Rozendaal, Romain Lepert, Todor Boinovski, Mart van Baalen, Markus Nagel, Paul N. Whatmough, Babak Ehteshami Bejnordi:
Mixture of Cache-Conditional Experts for Efficient Mobile Device Inference. - Noaman Mehmood, Kenneth E. Barner:
Disentangled Embedding through Style and Mutual Information for Domain Generalization. - Ayoub Echchahed, Pablo Samuel Castro:
A Survey of State Representation Learning for Deep Reinforcement Learning. - Giang Nguyen, Ivan Brugere, Shubham Sharma, Sanjay Kariyappa, Anh Totti Nguyen, Freddy Lécué:
Interpretable LLM-based Table Question Answering. - Blagoj Mitrevski, Arina Rak, Julian Schnitzler, Chengkun Li, Andrii Maksai, Jesse Berent, Claudiu Cristian Musat:
InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write. - Liu Yang, Fabian Paischer, Kaveh Hassani, Jiacheng Li, Shuai Shao, Zhang Gabriel Li, Yun He, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Robert D. Nowak, Xiaoli Gao, Hamid Eghbalzadeh:
Unifying Generative and Dense Retrieval for Sequential Recommendation. - Keanu Sisouk, Julie Delon, Julien Tierny:
A User's Guide to Sampling Strategies for Sliced Optimal Transport. - Yukti Makhija, Samarth Bhatia, Manoj Kumar, Sandeep Kumar:
Modularity aided consistent attributed graph clustering via coarsening. - Yash Sinha, Murari Mandal, Mohan S. Kankanhalli:
UnSTAR: Unlearning with Self-Taught Anti-Sample Reasoning for LLMs. - Jipeng Lyu, Jiahua Dong, Yu-Xiong Wang:
SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis. - Sarah Dean, Evan Dong, Meena Jagadeesan, Liu Leqi:
Accounting for AI and Users Shaping One Another: The Role of Mathematical Models. - Mohammad Hassan Vali, Tom Bäckström:
Unsupervised Panoptic Interpretation of Latent Spaces in GANs Using Space-Filling Vector Quantization. - Takumi Fukami, Tomoya Murata, Kenta Niwa:
Adaptive Clipping for Differential Private Federated Learning in Interpolation Regimes. - Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty, Rémi Flamary, Pascal Frossard, Titouan Vayer:
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein. - Tian Qin, Wei-Min Huang:
Riemann-Lebesgue Forest for Regression. - Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier:
A Survey of Reinforcement Learning from Human Feedback. - Mattia Opper, Roland Fernandez, Paul Smolensky, Jianfeng Gao:
TRA: Better Length Generalisation with Threshold Relative Attention. - Ciwan Ceylan, Kambiz Ghoorchian, Danica Kragic:
Disobeying Directions: Switching Random Walk Filters for Unsupervised Node Embedding Learning on Directed Graphs. - Yunpeng Jiang, Yutong Ban, Paul Weng:
Understanding and Reducing the Class-Dependent Effects of Data Augmentation with A Two-Player Game Approach. - Xiangqi Wang, Shaokun Zhang, Jose Efraim Aguilar Escamill, Qingyun Wu, Xiangliang Zhang, Jian Kang, Huazheng Wang:
Fair Online Influence Maximization. - Songhan Zhang, ShiNung Ching:
A Stochastic Polynomial Expansion for Uncertainty Propagation through Networks. - Fang Sun, Zijie Huang, Haixin Wang, Huacong Tang, Xiao Luo, Wei Wang, Yizhou Sun:
Graph Fourier Neural ODEs: Modeling Spatial-temporal Multi-scales in Molecular Dynamics. - Zarif Ikram, Ling Pan, Dianbo Liu:
Evolution guided generative flow networks. - Guy Barzilai, Ohad Shamir, Moslem Zamani:
Are Convex Optimization Curves Convex? - Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone, Marco Ciccone:
Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum. - Yang Sui, Huy Phan, Jinqi Xiao, Tianfang Zhang, Zijie Tang, Cong Shi, Yan Wang, Yingying Chen, Bo Yuan:
DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models. - Yingheng Wang, Zichen Wang, Gil Sadeh, Luca Zancato, Alessandro Achille, George Karypis, Huzefa Rangwala:
LC-PLM: Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers. - Naveen Janaki Raman, Mateo Espinosa Zarlenga, Juyeon Heo, Mateja Jamnik:
Do Concept Bottleneck Models Respect Localities? - Jing Sun, Cong Zhang, Zhiguang Cao:
Collaboration with Dynamic Open Ad Hoc Team via Team State Modelling. - Leonardo Cotta, Chris J. Maddison:
Test-Time Fairness and Robustness in Large Language Models. - Weiyang Zhang, Xinyang Chen, Yu Sun, Weili Guan, Liqiang Nie:
Batch Training for Streaming Time Series: A Transferable Augmentation Framework to Combat Distribution Shifts. - Alberto Caron, Vasilios Mavroudis, Chris Hicks:
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning. - Ziyi Zhang, Yorie Nakahira, Guannan Qu:
Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information. - Jiazhi Li, Mahyar Khayatkhoei, Jiageng Zhu, Hanchen Xie, Mohamed E. Hussein, Wael AbdAlmageed:
Fairness and Disentanglement: A Critical Review of Predominant Bias in Neural Networks. - Federico Di Gennaro, Thibault Laugel, Vincent Grari, Marcin Detyniecki:
Controlled Model Debiasing through Minimal and Interpretable Updates. - Roei Schuster, Jin Peng Zhou, Thorsten Eisenhofer, Paul Grubbs, Nicolas Papernot:
Learned-Database Systems Security. - Matthew Bowditch, Mike Paterson, Matthias Englert, Ranko Lazic:
Variance Dichotomy in Feature Spaces of Facial Recognition Systems is a Weak Defense against Simple Weight Manipulation Attacks. - Kim-Celine Kahl, Selen Erkan, Jeremias Traub, Carsten T. Lüth, Klaus H. Maier-Hein, Lena Maier-Hein, Paul F. Jaeger:
SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks. - Prateek Yadav, Tu Vu, Jonathan Lai, Alexandra Chronopoulou, Manaal Faruqui, Mohit Bansal, Tsendsuren Munkhdalai:
What Matters for Model Merging at Scale? - Tobias Ladner, Matthias Althoff:
Fully Automatic Neural Network Reduction for Formal Verification. - Timothée Darcet, Federico Baldassarre, Maxime Oquab, Julien Mairal, Piotr Bojanowski:
Cluster and Predict Latents Patches for Improved Masked Image Modeling. - Huajun Xi, Jianguo Huang, Kangdao Liu, Lei Feng, Hongxin Wei:
Does confidence calibration improve conformal prediction? - Pegah Nokhiz, Aravinda Kanchana Ruwanpathirana, Aditya Bhaskara, Suresh Venkatasubramanian:
Counting Hours, Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security. - Wenbo Zhang, Hengrui Cai:
Where to Intervene: Action Selection in Deep Reinforcement Learning. - Arpan Mukherjee, Shashanka Ubaru, Keerthiram Murugesan, Karthikeyan Shanmugam, Ali Tajer:
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing. - Tejaswini Pedapati, Amit Dhurandhar, Soumya Ghosh, Soham Dan, Prasanna Sattigeri:
Large Language Model Confidence Estimation via Black-Box Access. - Jenny Y. Huang, David R. Burt, Yunyi Shen, Tin D. Nguyen, Tamara Broderick:
Approximations to worst-case data dropping: unmasking failure modes. - Paribesh Regmi, Rui Li, Kishan K. C.:
Bayesian Neighborhood Adaptation for Graph Neural Networks. - Qi Hu, Weifeng Jiang, Haoran Li, Zihao Wang, Jiaxin Bai, Qianren Mao, Yangqiu Song, Lixin Fan, Jianxin Li:
Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs. - Hengyue Liang, Taihui Li, Ju Sun:
A Baseline Method for Removing Invisible Image Watermarks using Deep Image Prior. - Jonathan Drechsel, Anja Reusch, Steffen Herbold:
MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training. - Mattia Rosso, Juho Ylä-Jääski, Zheyang Shen, Markus Heinonen, Maurizio Filippone:
Gaussian Processes with Bayesian Inference of Covariate Couplings. - Abhinav Moudgil, Boris Knyazev, Guillaume Lajoie, Eugene Belilovsky:
Celo: Training Versatile Learned Optimizers on a Compute Diet. - Lea Richtmann, Viktoria-Sophie Schmiesing, Dennis Wilken, Jan Heine, Aaron D. Tranter, Avishek Anand, Tobias J. Osborne, Michèle Heurs:
Model-free reinforcement learning with noisy actions for automated experimental control in optics. - Md Ferdous Alam, Faez Ahmed:
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors. - Mathieu Ravaut, Bosheng Ding, Fangkai Jiao, Hailin Chen, Xingxuan Li, Ruochen Zhao, Chengwei Qin, Caiming Xiong, Shafiq Joty:
A Comprehensive Survey of Contamination Detection Methods in Large Language Models. - Pouria Mahdavinia, Mehrdad Mahdavi:
Low-rank Momentum Factorization for Memory Efficient Training. - Zhixu Tao, Ian Mason, Sanjeev R. Kulkarni, Xavier Boix:
Task Arithmetic Through The Lens Of One-Shot Federated Learning. - Xiangyu Sun, Raquel Aoki, Kevin H. Wilson:
No $D_{train}$: Model-Agnostic Counterfactual Explanations Using Reinforcement Learning. - Gbètondji J.-S. Dovonon, Michael M. Bronstein, Matt J. Kusner:
Setting the Record Straight on Transformer Oversmoothing. - Louis Bagot, Lucas Nunes Alegre, Steven Latré, Kevin Mets, Bruno Castro da Silva:
Successor Clusters: A Behavior Basis for Unsupervised Zero-Shot Reinforcement Learning. - Alfredo Reichlin, Miguel Vasco, Danica Kragic:
Walking on the Fiber: A Simple Geometric Approximation for Bayesian Neural Networks. - Nicolas Guérin, Ryan M. Nefdt, Emmanuel Chemla:
Qualifying Knowledge and Knowledge Sharing in Multilingual Models. - Johanna D'Ciofalo Khodaverdian, Eric Banzuzi, Katharina Deckenbach:
A Reproducibility Study of Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks. - Firas Laakom, Moncef Gabbouj, Jürgen Schmidhuber, Yuheng Bu:
Class-wise Generalization Error: an Information-Theoretic analysis. - Juncheng Liu, Chenghao Liu, Gerald Woo, Yiwei Wang, Bryan Hooi, Caiming Xiong, Doyen Sahoo:
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting. - Dylan Sam, Devin Willmott, João D. Semedo, J. Zico Kolter:
Finetuning CLIP to Reason about Pairwise Differences. - Lei Shen, Nan Pu, Zhun Zhong, Mingming Gong, Dianhai Yu, Chengqi Zhang, Bo Han:
Federated Generalized Novel Category Discovery with Prompts Tuning. - Zergham Ahmed, Joshua B. Tenenbaum, Christopher Bates, Samuel J. Gershman:
Synthesizing world models for bilevel planning. - Yoshikazu Terada, Xin Guan:
A note on the $k$-means clustering for missing data. - Mohsen Amidzadeh, Mario Di Francesco:
FB-MOAC: A Reinforcement Learning Algorithm for Forward-Backward Markov Decision Processes. - Jie Shen:
Towards Efficient Contrastive PAC Learning. - Kaiwen Dong, Haitao Mao, Zhichun Guo, Nitesh V. Chawla:
Universal Link Predictor By In-Context Learning on Graphs. - Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, Yuxian Gu, Xin Cheng, Xun Wang, Si-Qing Chen, Li Dong, Wei Lu, Zhifang Sui, Benyou Wang, Wai Lam, Furu Wei:
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models. - Yusuf Sale, Aaditya Ramdas:
Online Selective Conformal Inference: Errors and Solutions. - Yiming Qin, Clément Vignac, Pascal Frossard:
SparseDiff: Sparse Discrete Diffusion for Scalable Graph Generation. - Ruibo Ming, Zhewei Huang, Jingwei Wu, Zhuoxuan Ju, Daxin Jiang, Jianming Hu, Lihui Peng, Shuchang Zhou:
A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches. - Dante Campregher, Yanxu Chen, Sander Hoffman, Maria Heuss:
Tracing Facts or just Copies? A critical investigation of the Competitions of Mechanisms in Large Language Models. - Wes Camp:
Algorithmic fairness with monotone likelihood ratios. - Cheuk Ting Leung, Rohan Ghosh, Mehul Motani:
Towards Robust Scale-Invariant Mutual Information Estimators. - Veronica Lachi, Alice Moallemy-Oureh, Andreas Roth, Pascal Welke:
Expressive Pooling for Graph Neural Networks. - Liyi Zhang, Michael Y. Li, R. Thomas McCoy, Theodore R. Sumers, Jian-Qiao Zhu, Thomas L. Griffiths:
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions. - Fin Amin, Jung-Eun Kim:
The Over-Certainty Phenomenon in Modern Test-Time Adaptation Algorithms. - Ahmet Hamdi Güzel, Ilija Bogunovic, Jack Parker-Holder:
Synthetic Data is Sufficient for Zero-Shot Visual Generalization from Offline Data. - Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang, Jiahao Xiao, Nan Tang, Si-Hang Yang, Chengxing Jia, Ming-Kun Xie, Xiang Chen, Sheng-Jun Huang, Yang Yu:
Interactive Large Language Models for Reliable Answering under Incomplete Context. - Daniel Jiwoong Im, Kevin Zhang, Nakul Verma, Kyunghyun Cho:
Deep Autoregressive Models as Causal Inference Engines. - Simon Matthieu Ferreira, Charles K. Assaad:
Identifying Macro Causal Effects in a C-DMG over ADMGs. - Manato Yaguchi, Kotaro Sakamoto, Ryosuke Sakamoto, Masato Tanabe, Masatomo Akagawa, Yusuke Hayashi, Masahiro Suzuki, Yutaka Matsuo:
The Geometry of Phase Transitions in Diffusion Models: Tubular Neighbourhoods and Singularities. - Léa Demeule, Mahtab Sandhu, Glen Berseth:
Adaptive Resolution Residual Networks - Generalizing Across Resolutions Easily and Efficiently. - Prajna G. Malettira, Shubham Negi, Wachirawit Ponghiran, Kaushik Roy:
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks. - Zixuan Ke, Fangkai Jiao, Yifei Ming, Xuan-Phi Nguyen, Austin Xu, Do Xuan Long, Minzhi Li, Chengwei Qin, Peifeng Wang, Silvio Savarese, Caiming Xiong, Shafiq Joty:
A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems. - Adam Tupper, Christian Gagné:
Revisiting Data Augmentation for Ultrasound Images. - Stefan Balauca, Mark Niklas Müller, Yuhao Mao, Maximilian Baader, Marc Fischer, Martin T. Vechev:
Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations. - Tijs Wiegman, Leyla Perotti, Viktória Pravdová, Ori Brand, Maria Heuss:
Reproducibility study of "Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals". - Minh Nguyen, Batuhan K. Karaman, Heejong Kim, Alan Q. Wang, Fengbei Liu, Mert R. Sabuncu:
Knockout: A simple way to handle missing inputs. - Hsing-Huan Chung, Shravan Chaudhari, Xing Han, Yoav Wald, Suchi Saria, Joydeep Ghosh:
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning. - Dandan Guo, Zhuo Li, He Zhao, Mingyuan Zhou, Hongyuan Zha:
Diverse Condensed Data Generation via Class Preserving Distribution Matching. - Lu Wang, Fangkai Yang, Chaoyun Zhang, Junting Lu, Jiaxu Qian, Shilin He, Pu Zhao, Bo Qiao, He Huang, Si Qin, Qisheng Su, Jiayi Ye, Yudi Zhang, Jian-Guang Lou, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang:
Large Action Models: From Inception to Implementation. - Sara Folchini, Viplove Arora, Sebastian Goldt:
Exploring the potential of Direct Feedback Alignment for Continual Learning. - Sai Qian Zhang, Ziyun Li, Chuan Guo, Saeed Mahloujifar, Deeksha Dangwal, G. Edward Suh, Barbara De Salvo, Chiao Liu:
Unlocking Visual Secrets: Inverting Features with Diffusion Priors for Image Reconstruction. - Roy Ganz, Michael Elad:
Text-to-Image Generation Via Energy-Based CLIP. - Rolf A. N. Starre, Sammie Katt, Mustafa Mert Çelikok, Marco Loog, Frans A. Oliehoek:
Abstraction for Bayesian Reinforcement Learning in Factored POMDPs. - Soumava Paul, Prakhar Kaushik, Alan L. Yuille:
Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors. - Victor Rielly, Kamel Lahouel, Ethan Lew, Nicholas Fisher, Vicky Haney, Michael Wells, Bruno M. Jedynak:
MOCK: an Algorithm for Learning Nonparametric Differential Equations via Multivariate Occupation Kernel Functions. - Haoyuan Sun, Bin Liang, Bo Xia, Jiaqi Wu, Yifei Zhao, Kai Qin, Yongzhe Chang, Xueqian Wang:
Diffusion-RainbowPA: Improvements Integrated Preference Alignment for Diffusion-based Text-to-Image Generation. - Calvin McCarter:
Unmasking Trees for Tabular Data. - Jiaxu Liu, Xinping Yi, Xiangyu Yin, Yuhang Song, Gaojie Jin, Xiaowei Huang:
Toward Linearly Regularizing the Geometric Bottleneck of Linear Generalized Attention. - Yu Inatsu:
Bayesian Optimization of Robustness Measures under Input Uncertainty: A Randomized Gaussian Process Upper Confidence Bound Approach. - Jiangtao Kong, Zhenyu Zong, Tianyi Zhou, Huajie Shao:
YoooP: You Only Optimize One Prototype per Class for Non-Exemplar Incremental Learning. - Peiyuan Zhang, Kaichen Zhang, Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, Ziwei Liu:
Long Context Transfer from Language to Vision. - Isaac Ellmen, Constantin Schneider, Matthew I. J. Raybould, Charlotte M. Deane:
Transformers trained on proteins can learn to attend to Euclidean distance. - Sujan Sai Gannamaneni, Rohil Prakash Rao, Michael Mock, Maram Akila, Stefan Wrobel:
Detecting Systematic Weaknesses in Vision Models along Predefined Human-Understandable Dimensions. - Neeru Dubey, Elin Karlsson, Miguel Angel Redondo, Johan Reimegård, Anna Rising, Hedvig Kjellström:
Customizing Spider Silk: Generative Models with Mechanical Property Conditioning for Protein Engineering. - Soon Hoe Lim, Yijin Wang, Annan Yu, Emma Hart, Michael W. Mahoney, Xiaoye Sherry Li, N. Benjamin Erichson:
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting. - Zora Che, Stephen Casper, Robert Kirk, Anirudh Satheesh, Stewart Slocum, Lev E. McKinney, Rohit Gandikota, Aidan Ewart, Domenic Rosati, Zichu Wu, Zikui Cai, Bilal Chughtai, Yarin Gal, Furong Huang, Dylan Hadfield-Menell:
Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities. - Yifan Zhang, Jingqin Yang, Yang Yuan, Andrew C. Yao:
Cumulative Reasoning with Large Language Models. - Elliot Layne, Jason S. Hartford, Sébastien Lachapelle, Mathieu Blanchette, Dhanya Sridhar:
Sparsity regularization via tree-structured environments for disentangled representations. - Leona Hennig, Marius Lindauer:
Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks. - Rishabh Anand, Chaitanya K. Joshi, Alex Morehead, Arian Rokkum Jamasb, Charles Harris, Simon V. Mathis, Kieran Didi, Rex Ying, Bryan Hooi, Pietro Lio:
RNA-FrameFlow: Flow Matching for de novo 3D RNA Backbone Design. - Semih Cayci, Yilin Zheng, Atilla Eryilmaz:
Budgeted-Bandits with Controlled Restarts with Applications in Learning and Computing. - Junjie Wu, Tsz Ting Chung, Kai Chen, Dit-Yan Yeung:
Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Models. - Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, Wei-Chih Hung, Jingwei Ji, Kristy Choi, Di Huang, Tong He, Paul Covington, Benjamin Sapp, Yin Zhou, James Guo, Dragomir Anguelov, Mingxing Tan:
EMMA: End-to-End Multimodal Model for Autonomous Driving. - Lucas Möller, Pascal Tilli, Ngoc Thang Vu, Sebastian Padó:
Explaining Caption-Image Interactions in CLIP Models with Second-Order Attributions. - Fabian Paischer, Liu Yang, Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky T. Q. Chen, Zhang Gabriel Li, Xiaoli Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadeh:
Preference Discerning with LLM-Enhanced Generative Retrieval. - Sota Kudo:
Label Smoothing is a Pragmatic Information Bottleneck. - Aniket Roy, Maitreya Suin, Anshul Shah, Ketul Shah, Jiang Liu, Rama Chellappa:
DiffNat : Exploiting the Kurtosis Concentration Property for Image quality improvement. - Naoyuki Terashita, Satoshi Hara:
Variance Reduction of Stochastic Hypergradient Estimation by Mixed Fixed-Point Iteration. - Saheb Chhabra, Kartik Thakral, Surbhi Mittal, Mayank Vatsa, Richa Singh:
DIVINE: Diverse-Inconspicuous Feature Learning to Mitigate Abridge Learning. - Yufeng Yang, Erin E. Tripp, Yifan Sun, Shaofeng Zou, Yi Zhou:
Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization. - Aleksandar Todorov, Juan Cardenas-Cartagena, Rafael Fernandes Cunha, Marco Zullich, Matthia Sabatelli:
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning. - Yuzhou Chen, Xiao Guo, Shujie Ma:
Stochastic Block Model-Aware Topological Neural Networks for Graph Link Prediction. - Tooba Imtiaz, Morgan Kohler, Jared Miller, Zifeng Wang, Masih Eskandar, Mario Sznaier, Octavia I. Camps, Jennifer G. Dy:
SAIF: Sparse Adversarial and Imperceptible Attack Framework. - Haixiang Zhang, Baturalp Yalcin, Javad Lavaei, Eduardo D. Sontag:
Exact Recovery Guarantees for Parameterized Nonlinear System Identification Problem under Sparse Disturbances or Semi-Oblivious Attacks. - Usman Gohar, Zeyu Tang, Jialu Wang, Kun Zhang, Peter Spirtes, Yang Liu, Lu Cheng:
Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challenges. - Yanis Lalou, Théo Gnassounou, Antoine Collas, Antoine de Mathelin, Ambroise Odonnat, Thomas Moreau, Alexandre Gramfort, Rémi Flamary:
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities. - Adwait Datar, Nihat Ay:
Convergence Properties of Natural Gradient Descent for Minimizing KL Divergence. - Steven Kolawole, Don Kurian Dennis, Ameet Talwalkar, Virginia Smith:
Agreement-Based Cascading for Efficient Inference. - Jose Cribeiro-Ramallo, Federico Matteucci, Paul Enciu, Alexander Jenke, Vadim Arzamasov, Thorsten Strufe, Klemens Böhm:
Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data. - Tobias Kortus, Ralf Keidel, Nicolas R. Gauger:
Exploring End-to-end Differentiable Neural Charged Particle Tracking - A Loss Landscape Perspective. - Zitao Shuai, Chenwei Wu, Zhengxu Tang, Liyue Shen:
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity. - Yihan Wang, Yiwei Lu, Guojun Zhang, Franziska Boenisch, Adam Dziedzic, Yaoliang Yu, Xiao-Shan Gao:
MUC: Machine Unlearning for Contrastive Learning with Black-box Evaluation. - Xiasi Wang, Jiaqi Lin, Chaoqi Chen, Luyao Tang, Yi Huang, Chengsen Wang, Lei Ye, Yuan Yao:
Activate and Adapt: A Two-Stage Framework for Open-Set Model Adaptation. - Thanawat Lodkaew, Tongtong Fang, Takashi Ishida, Masashi Sugiyama:
Importance Weighting for Aligning Language Models under Deployment Distribution Shift. - Nir Ben-Ari, Amitai Yacobi, Uri Shaham:
Generalizable Spectral Embedding with an Application to UMAP. - Jinxin Xiong, Xi Gao, Linxin Yang, Jiang Xue, Xiaodong Luo, Akang Wang:
Solving Quadratic Programs via Deep Unrolled Douglas-Rachford Splitting. - Mohammad Mahdi Omati, Prabhu Babu, Petre Stoica, Arash Amini:
A Max-Min Approach to the Worst-Case Class Separation Problem. - Nicholas Mehlman, Jean-Christophe Gagnon-Audet, Michael Shvartsman, Kelvin Niu, Alexander H. Miller, Shagun Sodhani:
Scaling and Distilling Transformer Models for sEMG. - Evgeniia Tokarchuk, Hua Chang Bakker, Vlad Niculae:
Keep your distance: learning dispersed embeddings on $\mathbb{S}_{m}$. - Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, Chunyuan Li:
LLaVA-Video: Video Instruction Tuning With Synthetic Data. - Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Mingxuan Ju, Neil Shah, Nitesh V. Chawla:
Node Duplication Improves Cold-start Link Prediction. - Simone Piaggesi, André Panisson, Megha Khosla:
Disentangled and Self-Explainable Node Representation Learning. - Geonwoo Ko, Sungyeob Yoo, Seri Ham, Seeyeon Kim, Minkyu Kim, Joo-Young Kim:
SuFP: Piecewise Bit Allocation Floating-Point for Robust Neural Network Quantization. - Aleksandr Dremov, Alexander Hägele, Atli Kosson, Martin Jaggi:
Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler. - Amit Zalcher, Navve Wasserman, Roman Beliy, Oliver Heinimann, Michal Irani:
Don't Judge Before You CLIP: A Unified Approach for Perceptual Tasks. - Duo Xu, Faramarz Fekri:
HDCS: Hierarchy Discovery and Critic Shaping for Reinforcement Learning with Automaton Specification. - An Thai Le, Khai Nguyen, Minh Nhat Vu, Joao Carvalho, Jan Peters:
Model Tensor Planning. - Gerrit Welper, Benjamin Keene:
Approximation, Estimation and Optimization Errors for a Deep Neural Network. - Yilun Zhou, Caiming Xiong, Silvio Savarese, Chien-Sheng Wu:
Shared Imagination: LLMs Hallucinate Alike. - Thomas F. Burns, Tomoki Fukai, Christopher J. Earls:
Associative memory inspires improvements for in-context learning using a novel attention residual stream architecture. - Christopher Lohse, Jonas Wahl:
Sortability of Time Series Data. - Tobias Möller, Borun Shi:
Formulating Node Labelling as Node Classification or Link Prediction in Different Graph Representations. - Benjamin Plaut, Nguyen X. Khanh, Tu Trinh:
Probabilities of Chat LLMs Are Miscalibrated but Still Predict Correctness on Multiple-Choice Q&A. - Lukas Koller, Tobias Ladner, Matthias Althoff:
Set-Based Training for Neural Network Verification. - Usman Anwar, Johannes von Oswald, Louis Kirsch, David Krueger, Spencer Frei:
Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens. - Liya Guo, Zun Wang, Chang Liu, Junzhe Li, Pipi Hu, Yi Zhu, Tao Qin:
Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidance. - Yang Sui, Yu-Neng Chuang, Guanchu Wang, Jiamu Zhang, Tianyi Zhang, Jiayi Yuan, Hongyi Liu, Andrew Wen, Shaochen Zhong, Na Zou, Hanjie Chen, Xia Hu:
Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models. - Vaibhav Seth, Ayan Sengupta, Arinjay Pathak, Aastha A K. Verma, Natraj Raman, Sriram Gopalakrishnan, Niladri Chatterjee, Tanmoy Chakraborty:
Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation. - Michael T. Wojnowicz, Kaitlin Gili, Preetish Rath, Eric L. Miller, Jeffrey Miller, Clifford Hancock, Meghan O'Donovan, Seth Elkin-Frankston, Tad Brunyé, Michael C. Hughes:
Discovering group dynamics in coordinated time series via hierarchical recurrent switching-state models. - Wenduo Cheng, Junhong Shen, Mikhail Khodak, Jian Ma, Ameet Talwalkar:
L2G: Repurposing Language Models for Genomics Tasks. - Jelle Luijkx, Zlatan Ajanovic, Laura Ferranti, Jens Kober:
ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning. - Zijun Cui, Sam Griesemer, Sungyong Seo, Joshua Hikida, Yan Liu:
Physics-Aware Spatiotemporal Causal Graph Network for Forecasting with Limited Data. - Kalle Kujanpää, Pekka Marttinen, Harri Valpola, Alexander Ilin:
Efficient Knowledge Injection in LLMs via Self-Distillation. - Johannes Hertrich, Sebastian Neumayer:
Generative Feature Training of Thin 2-Layer Networks. - Wei Liu, Anweshit Panda, Ujwal Pandey, Christopher Brissette, Yikang Shen, George M. Slota, Naigang Wang, Jie Chen, Yangyang Xu:
Compressed Decentralized Momentum Stochastic Gradient Methods for Nonconvex Optimization. - Stephen Casper, Lennart Schulze, Oam Patel, Dylan Hadfield-Menell:
Defending Against Unforeseen Failure Modes with Latent Adversarial Training. - Cedric Maron, Virginie Fresse, Mathieu Orzalesi:
One-Shot Federated Distillation Using Monoclass Teachers: A Study of Knowledge Fragmentation and Out-of-Distribution Supervision. - Vignesh Kothapalli, Tianyu Pang, Shenyang Deng, Zongmin Liu, Yaoqing Yang:
From Spikes to Heavy Tails: Unveiling the Spectral Evolution of Neural Networks. - Makanjuola Adekunmi Ogunleye, Chase Vickery, Ismini Lourentzou:
Emergent Corpus Pre-training Benefits Vision Language Models. - Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Nathaniel Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J. Zico Kolter:
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation. - Evan Yao, Retsef Levi, Assaf Avrahami, Abraham Meidan:
Explaining Confident Black-Box Predictions. - Xingyou Song, Dara Bahri:
Decoding-based Regression. - Bicheng Xu, Qi Yan, Renjie Liao, Lele Wang, Leonid Sigal:
Joint Generative Modeling of Grounded Scene Graphs and Images via Diffusion Models. - Sonia Raychaudhuri, Angel X. Chang:
Semantic Mapping in Indoor Embodied AI - A Survey on Advances, Challenges, and Future Directions. - Arwen Bradley, Preetum Nakkiran:
Classifier-Free Guidance is a Predictor-Corrector. - Viet-Hung Tran, Ngoc-Bao Nguyen, Son T. Mai, Hans Vandierendonck, Ira Assent, Alex C. Kot, Ngai-Man Cheung:
Random Erasing vs. Model Inversion: A Promising Defense or a False Hope? - Edan Kinderman, Itay Hubara, Haggai Maron, Daniel Soudry:
Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks. - Peijia Qin, Ruiyi Zhang, Pengtao Xie:
BiDoRA: Bi-level Optimization-Based Weight-Decomposed Low-Rank Adaptation. - Olawale Salaudeen, Nicole Chiou, Shiny Weng, Sanmi Koyejo:
Are Domain Generalization Benchmarks with Accuracy on the Line Misspecified? - Yuma Ichikawa, Hiroaki Iwashita:
Continuous Parallel Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems. - Johann Brehmer, Sönke Behrends, Pim de Haan, Taco Cohen:
Does equivariance matter at scale? - Jesse Hoogland, George Wang, Matthew Farrugia-Roberts, Liam Carroll, Susan Wei, Daniel Murfet:
Loss Landscape Degeneracy and Stagewise Development in Transformers. - Satchel Grant, Noah Goodman, James L. McClelland:
Emergent Symbol-like Number Variables in Artificial Neural Networks. - Nedeljko Radulovic, Albert Bifet, Fabian M. Suchanek:
BELLA: Black-box model Explanations by Local Linear Approximations. - Masahiro Fujisawa, Futoshi Futami:
On the Convergence of SVGD in KL divergence via Approximate gradient flow. - Atharva Abhijit Tambat, Durga Sivasubramanian, Ganesh Ramakrishnan, Pradeep Shenoy:
Unified Wisdom: Harnessing Collaborative Learning to Improve Efficacy of Knowledge Distillation. - Simone Antonelli, Aleksandar Bojchevski:
Node-Level Data Valuation on Graphs. - Ali Dadras, Sebastian U. Stich, Alp Yurtsever:
Personalized Federated Learning via Low-Rank Matrix Optimization. - Yuzhou Zhao, J. Matías Di Martino, Amirhossein Farzam, Guillermo Sapiro:
FoldDiff: Folding in Point Cloud Diffusion. - Eméric Gbaguidi:
A stochastic gradient descent algorithm with random search directions. - Yaswanth Narsupalli, Abhranil Chandra, Sreevatsa Muppirala, Manish Gupta, Pawan Goyal:
ReFeR: Improving Evaluation and Reasoning through Hierarchy of Models. - Myung Jun Kim, Félix Lefebvre, Gaëtan Brison, Alexandre Perez-Lebel, Gaël Varoquaux:
Table Foundation Models: on knowledge pre-training for tabular learning. - Unique Subedi, Ambuj Tewari:
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators. - Andi Nika, Sepehr Elahi, Çagin Ararat, Cem Tekin:
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive Discretization. - Saptarshi Roy, Subha Maity, Songkai Xue, Mikhail Yurochkin, Yuekai Sun:
How does overparametrization affect performance on minority groups? - Vasisht Duddu, Rui Zhang, N. Asokan:
Combining Machine Learning Defenses without Conflicts. - Barak Pinkovich, Boaz Matalon, Ehud Rivlin, Héctor Rotstein:
MESSI: A Multi-Elevation Semantic Segmentation Image Dataset of an Urban Environment. - Emanuele Sansone, Robin Manhaeve:
Unifying Self-Supervised Clustering and Energy-Based Models. - Armin Saghafian, Amirmohammad Izadi, Negin Hashemi Dijujin, Mahdieh Soleymani Baghshah:
CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives. - Jaeyeon Kim, Sehyun Kwon, Joo Young Choi, Jongho Park, Jaewoong Cho, Jason D. Lee, Ernest K. Ryu:
Task Diversity Shortens the In-Context Learning Plateau. - Md. Ismail Hossain, Mirza M. Lutfe Elahi, Sameera Ramasinghe, Ali Cheraghian, Fuad Rahman, Nabeel Mohammed, Shafin Rahman:
LumiNet: Perception-Driven Knowledge Distillation via Statistical Logit Calibration. - Tal Reiss, Bar Cavia, Yedid Hoshen:
Enabling Users to Falsify Deepfake Attacks. - Andrea Napoli, Paul White:
Clustering-Based Validation Splits for Model Selection under Domain Shift. - Christian Camaño, Daniel Huang:
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation. - Fabrizio Sandri, Elia Cunegatti, Giovanni Iacca:
2SSP: A Two-Stage Framework for Structured Pruning of LLMs. - Yanxia Deng, Aozhong Zhang, Selcuk Gurses, Naigang Wang, Zi Yang, Penghang Yin:
CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization. - Kaiyu He, Zhiyu Chen:
From Reasoning to Learning: A Survey on Hypothesis Discovery and Rule Learning with Large Language Models. - Leon Lang, Patrick Forré:
Modeling Human Beliefs about AI Behavior for Scalable Oversight. - Ashutosh Kumar Nirala, Jin Tian, Olukorede Fakorede, Modeste Atsague:
AlignFix: Fixing Adversarial Perturbations by Agreement Checking for Adversarial Robustness against Black-box Attacks. - Shreyas Malakarjun Patil, Cameron Ethan Taylor, Constantine Dovrolis:
How Can Knowledge of a Task's Modular Structure Improve Generalization and Training Efficiency? - Linus Ruben Bach, Emma Bakker, Rénan van Dijk, Jip de Vries, Konrad Szewczyk:
Registers in Small Vision Transformers: A Reproducibility Study of Vision Transformers Need Registers. - Abhay Sheshadri, Aidan Ewart, Phillip Guo, Aengus Lynch, Cindy Wu, Vivek Hebbar, Henry Sleight, Asa Cooper Stickland, Ethan Perez, Dylan Hadfield-Menell, Stephen Casper:
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs. - Mishal Fatima, Steffen Jung, Margret Keuper:
Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models. - Huawei Sun, Zixu Wang, Hao Feng, Julius Ott, Lorenzo Servadei, Robert Wille:
TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimation. - Yeho Gwon, Sehyun Hwang, Hoyoung Kim, Jungseul Ok, Suha Kwak:
Enhancing Cost Efficiency in Active Learning with Candidate Set Query. - Insoo Kim, Geonseok Seo, Hyong-Euk Lee, Jinwoo Shin:
RefDeblur: Blind Motion Deblurring with Self-Generated Reference Image. - Giang Do, Kha Pham, Hung Le, Truyen Tran:
On the Role of Discrete Representation in Sparse Mixture of Experts. - Hasan Abed Al Kader Hammoud, Bernard Ghanem:
DiffCLIP: Differential Attention Meets CLIP. - Zibo Liu, Zhe Jiang, Zelin Xu, Tingsong Xiao, Zhengkun Xiao, Yupu Zhang, Haibo Wang, Shigang Chen:
Spatio-temporal Partial Sensing Forecast of Long-term Traffic. - Benjamin Schneider, Florian Kerschbaum, Wenhu Chen:
ABC: Achieving Better Control of Visual Embeddings using VLLMs. - Muhammad Jehanzeb Mirza, Mengjie Zhao, Zhuoyuan Mao, Sivan Doveh, Wei Lin, Paul Gavrikov, Michael Dorkenwald, Shiqi Yang, Saurav Jha, Hiromi Wakaki, Yuki Mitsufuji, Horst Possegger, Rogério Feris, Leonid Karlinsky, James R. Glass:
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models. - Maosheng Yang, Geert Leus, Elvin Isufi:
Hodge-Aware Convolutional Learning on Simplicial Complexes. - Jonas M. Kübler, Yu-Xiang Wang, Shoham Sabach, Navid Ansari, Matthäus Kleindessner, Kailash Budhathoki, Volkan Cevher, George Karypis:
A Proximal Operator for Inducing 2:4-Sparsity. - Franck Iutzeler, Adrien Mazoyer:
Risk-controlling Prediction with Distributionally Robust Optimization. - Nurbek Tastan, Samuel Horváth, Karthik Nandakumar:
CYCle: Choosing Your Collaborators Wisely to Enhance Collaborative Fairness in Decentralized Learning. - Hari Chandana Kuchibhotla, Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian:
Efficient Vocabulary-Free Fine-Grained Visual Recognition in the Age of Multimodal LLMs. - Leonid Boytsov, Ameya Joshi, Filipe Condessa:
A Curious Case of Remarkable Resilience to Gradient Attacks via Fully Convolutional and Differentiable Front End with a Skip Connection. - Laura Manduchi, Clara Meister, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. - Moritz Piening, Robert Beinert:
Slicing the Gaussian Mixture Wasserstein Distance. - Farzaneh Dehghani, Mahsa Dibaji, Fahim Anzum, Lily Dey, Alican Basdemir, Sayeh Bayat, Jean-Christophe Boucher, Steve Drew, Sarah Elaine Eaton, Richard Frayne, Gouri Ginde, Ashley D. Harris, Yani Ioannou, Catherine Lebel, John Lysack, Leslie Salgado Arzuaga, Emma A. M. Stanley, Roberto Souza, Ronnie de Souza Santos, Lana Wells, Tyler Williamson, Matthias Wilms, Mark Ungrin, Marina L. Gavrilova, Mariana P. Bento:
Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems. - Haimin Zhang, Min Xu:
Genetic-Evolutionary Graph Neural Networks: A Paradigm for Improved Graph Representation Learning. - Sambhav Khurana, Xiner Li, Shurui Gui, Shuiwang Ji:
Hierarchical Language Model Design For Interpretable Graph Reasoning. - Bo Yuan, Jiaojiao Fan, Jiaming Liang, Yongxin Chen:
Client-only Distributed Markov Chain Monte Carlo Sampling over a Network. - Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann:
A Unified Approach Towards Active Learning and Out-of-Distribution Detection. - Kushal Raj Bhandari, Sixue Xing, Soham Dan, Jianxi Gao:
Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis. - Paul Darm, Annalisa Riccardi:
Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models. - Yizhe Ruan, Xuangeng Chu, Ziteng Cui, Yusuke Kurose, Junichi Iho, Youji Tokunaga, Makoto Horie, Yusaku Hayashi, Keisuke Nishizawa, Yasushi Koyama, Tatsuya Harada:
Enhancing Plaque Segmentation in CCTA with Prompt- based Diffusion Data Augmentation. - Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xin, Hongyi Wu:
PrivShap: A Finer-granularity Network Linearization Method for Private Inference. - Bang You, Huaping Liu, Jan Peters, Oleg Arenz:
Rollout Total Correlation for Deep Reinforcement Learning. - Yoshimitsu Morinishi, Shohei Shimizu:
Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding. - Rohan Asthana, Joschua Conrad, Maurits Ortmanns, Vasileios Belagiannis:
Dextr: Zero-Shot Neural Architecture Search with Singular Value Decomposition and Extrinsic Curvature. - Leonardo Ranaldi, Giulia Pucci, Fabio Massimo Zanzotto:
Transferring Reasoning Capabilities between LLMs operating via Curriculum Learning Policy. - Avi Cooper, Daniel Harari, Tomotake Sasaki, Spandan Madan, Hanspeter Pfister, Pawan Sinha, Xavier Boix:
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations. - Evelyn J. Mannix, Howard D. Bondell:
A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models. - Adamo Young, Fei Wang, David S. Wishart, Bo Wang, Russell Greiner, Hannes Rost:
FraGNNet: A Deep Probabilistic Model for Tandem Mass Spectrum Prediction. - Cecilia Ferrando, Daniel Sheldon:
Private Regression via Data-Dependent Sufficient Statistic Perturbation. - Sinong Geng, Houssam Nassif, Zhaobin Kuang, Anders Max Reppen, Ronnie Sircar:
Factor Learning Portfolio Optimization Informed by Continuous-Time Finance Models. - Lukas Rauch, René Heinrich, Ilyass Moummad, Alexis Joly, Bernhard Sick, Christoph Scholz:
Can Masked Autoencoders Also Listen to Birds? - Yifan Lan, Cai xin, Jun Cheng, Shan Tan:
Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition. - Oliver van Erven, Konstantinos Zafeirakis, Jacobus Smit, Julio Smidi, Luc Buijs:
[Re] Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents. - Fartash Faghri, Pavan Kumar Anasosalu Vasu, Cem Koc, Vaishaal Shankar, Alexander T. Toshev, Oncel Tuzel, Hadi Pouransari:
MobileCLIP2: Improving Multi-Modal Reinforced Training. - Muxing Wang, Pengkun Yang, Lili Su:
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments. - Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl, Till J. Bungert, Lukas Klein, Lars Krämer, Paul F. Jaeger, Fabian Isensee, Klaus H. Maier-Hein:
nnActive: A Framework for Evaluation of Active Learning in 3D Biomedical Segmentation. - Zhuo Zhi, Chen Feng, Adam Daneshmend, Mine Orlu, Andreas Demosthenous, Lu Yin, Da Li, Ziquan Liu, Miguel R. D. Rodrigues:
TFAR: A Training-Free Framework for Autonomous Reliable Reasoning in Visual Question Answering. - Yeshwanth Venkatesha, Souvik Kundu, Priyadarshini Panda:
Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits. - Kyle Gilman, David Hong, Jeffrey A. Fessler, Laura Balzano:
Streaming Heteroscedastic Probabilistic PCA with Missing Data. - Shalabh Bhatnagar, Deepak H. R:
Variance Reduced Smoothed Functional REINFORCE Policy Gradient Algorithms. - Jonas Berg Hansen, Andrea Cini, Filippo Maria Bianchi:
On Time Series Clustering with Graph Neural Networks. - Ali Mohaddes, Johannes Lederer:
Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning. - John T. Halloran, Manbir S. Gulati, Paul F. Roysdon:
Mamba State-Space Models Are Lyapunov-Stable Learners. - Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Bac Nguyen, Stefano Ermon, Yuki Mitsufuji:
G2D2: Gradient-Guided Discrete Diffusion for Inverse Problem Solving. - Shaocong Ma, Ziyi Chen, Yi Zhou, Heng Huang:
Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality. - Xingshuai Huang, Di Wu, Benoit Boulet:
Goal-Conditioned Data Augmentation for Offline Reinforcement Learning. - Yik Lun Kei, Hangjian Li, Yanzhen Chen, Oscar Hernan Madrid Padilla:
Change Point Detection on A Separable Model for Dynamic Networks. - Sergey Troshin, Vlad Niculae, Antske Fokkens:
On the Low-Rank Parametrization of Reward Models for Controlled Language Generation. - Jiachen Zhou, Han Xie, Carl Yang:
Graph Personalized Federated Learning via Client Network Learning. - Tomer Borreda, Daniel Freedman, Or Litany:
ReHub: Linear Complexity Graph Transformers with Adaptive Hub-Spoke Reassignment. - Chenxiang Zhang, Jun Pang, Sjouke Mauw:
Spurious Privacy Leakage in Neural Networks. - Nathan Phelps, Daniel J. Lizotte, Douglas G. Woolford:
Using Platt's scaling for calibration after undersampling - limitations and how to address them. - Sam Allen, David Ginsbourger, Johanna Ziegel:
Efficient pooling of predictions via kernel embeddings. - Erik Schultheis, Rohit Babbar:
Unbiased Loss Functions for Multilabel Classification with Missing Labels. - Shashank Agnihotri, Julian Yuya Caspary, Luca Schwarz, Xinyan Gao, Jenny Schmalfuss, Andrés Bruhn, Margret Keuper:
FlowBench: Benchmarking Optical Flow Estimation Methods for Reliability and Generalization. - Michelle Liu, Zhaocheng Zhu, Olexa Bilaniuk, Emmanuel Bengio:
Learning Equivalence Classes of Bayesian Network Structures with GFlowNet. - Chulabhaya Wijesundara, Andrea Baisero, Gregory David Castañón, Alan Carlin, Robert Platt, Christopher Amato:
Leveraging Fully-Observable Solutions for Improved Partially-Observable Offline Reinforcement Learning. - Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya:
Communication Cost Reduction for Subgraph Counting under Local Differential Privacy via Hash Functions. - Rishav Rishav, Somjit Nath, Vincent Michalski, Samira Ebrahimi Kahou:
Behaviour Discovery and Attribution for Explainable Reinforcement Learning. - Boyu Li, Haobin Jiang, Ziluo Ding, Xinrun Xu, Haoran Li, Dongbin Zhao, Zongqing Lu:
SELU: Self-Learning Embodied Multimodal Large Language Models in Unknown Environments. - Xiyan Xu, Sirui Xu, Yu-Xiong Wang, Liangyan Gui:
MoReact: Generating Reactive Motion from Textual Descriptions. - Philip Sosnin, Mark Niklas Müller, Maximilian Baader, Calvin Tsay, Matthew Wicker:
Certified Robustness to Data Poisoning in Gradient-Based Training. - Jérémie Donà, Benoit Gaujac, Timothy Atkinson, Liviu Copoiu, Thomas Pierrot, Thomas D. Barrett:
Learning the Language of Protein Structure. - Kaixuan Ji, Jiafan He, Quanquan Gu:
Reinforcement Learning from Human Feedback with Active Queries. - Roel Koopman, Amirreza Yousefzadeh, Mahyar Shahsavari, Guangzhi Tang, Manolis Sifalakis:
Exploring the Limitations of Layer Synchronization in Spiking Neural Networks. - Roy Siegelmann:
Complementarity: Toward Better Metrics and Optimizing Data Efficiency in LLMs. - Naoki Yoshida, Shogo Nakakita, Masaaki Imaizumi:
Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution. - Shuichi Nishino, Tomohiro Shiraishi, Teruyuki Katsuoka, Ichiro Takeuchi:
Statistical Test for Saliency Maps of Graph Neural Networks via Selective Inference. - Sara Kangaslahti, David Alvarez-Melis:
Continuous Language Model Interpolation yields Dynamic and Controllable Text Generation. - Tsiry Mayet, Simon Bernard, Romain Hérault, Clément Chatelain:
Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation. - Or Tal, Felix Kreuk, Yossi Adi:
Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation. - Difei Xu, Meng Ding, Zihang Xiang, Jinhui Xu, Di Wang:
Beyond ordinary Lipschitz constraints: Differentially Private optimization with TNC. - Huanqia Cai, Yijun Yang, Zhifeng Li:
System-2 Mathematical Reasoning via Enriched Instruction Tuning. - Jing Yu Koh, Stephen Marcus McAleer, Daniel Fried, Ruslan Salakhutdinov:
Tree Search for Language Model Agents. - Min-Hsuan Yeh, Max Kamachee, Seongheon Park, Yixuan Li:
HalluEntity: Benchmarking and Understanding Entity-Level Hallucination Detection. - Pooneh Mousavi, Gallil Maimon, Adel Moumen, Darius Petermann, Jiatong Shi, Haibin Wu, Haici Yang, Anastasia Kuznetsova, Artem Ploujnikov, Ricard Marxer, Bhuvana Ramabhadran, Benjamin Elizalde, Loren Lugosch, Jinyu Li, Cem Subakan, Philip C. Woodland, Minje Kim, Hung-yi Lee, Shinji Watanabe, Yossi Adi, Mirco Ravanelli:
Discrete Audio Tokens: More Than a Survey! - Ben Lewis, Thomas Moyse, James Parkinson, Elizabeth Telford, Callum Whitfield, Ranko Lazic:
Text to Stealthy Adversarial Face Masks. - Pingcheng Jian, Xiao Wei, Yanbaihui Liu, Samuel A. Moore, Michael M. Zavlanos, Boyuan Chen:
LAPP: Large Language Model Feedback for Preference-Driven Reinforcement Learning. - Shayan Gharib, Pierre-Alexandre Murena, Arto Klami:
Single-positive Multi-label Learning with Label Cardinality. - Constantin Ruhdorfer, Matteo Bortoletto, Anna Penzkofer, Andreas Bulling:
The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design. - Diogo Cruz:
Understanding the learned look-ahead behavior of chess neural networks. - Matthew C. Bendel, Saurav K. Shastri, Rizwan Ahmad, Philip Schniter:
Solving Inverse Problems using Diffusion with Iterative Colored Renoising. - Kai Cui, Sharif Azem, Christian Fabian, Kirill Kuroptev, Ramin Khalili, Osama Abboud, Florian Steinke, Heinz Koeppl:
Mean-Field RL for Large-Scale Unit-Capacity Pickup-and-Delivery Problems. - Naoufal El Bekri, Lucas Drumetz, Franck Vermet:
FlowKac: An Efficient Neural Fokker-Planck solver using Temporal Normalizing flows and the Feynman-Kac Formula. - Chen Bao, Jiarui Xu, Xiaolong Wang, Abhinav Gupta, Homanga Bharadhwaj:
HandsOnVLM: Vision-Language Models for Hand-Object Interaction Prediction. - Yang Xu, Chengchun Shi, Shikai Luo, Lan Wang, Rui Song:
Doubly Robust Uncertainty Quantification for Quantile Treatment Effects in Sequential Decision Making. - Laura Caspari, Alexander Trautsch, Michael Granitzer, Steffen Herbold:
Studying memorization of large language models using answers to Stack Overflow questions. - Jianxin Zhang, Clayton Scott:
Label Embedding via Low-Coherence Matrices. - Timothy Ossowski, Danyal Maqbool, Jixuan Chen, Zefan Cai, Tyler J. Bradshaw, Junjie Hu:
COMMA: A Communicative Multimodal Multi-Agent Benchmark. - Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo:
Efficient Object-Centric Representation Learning using Masked Generative Modeling. - Kai Yi, Georg Meinhardt, Laurent Condat, Peter Richtárik:
FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models. - Boyuan Wang, Richard Jiang:
DNR-Pruning: Sparsity-Aware Pruning via Dying Neuron Reactivation in Convolutional Neural Networks. - Bo Lei, Victor M. Castillo, Yeping Hu:
M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations. - Sanha Hwang, Seungpil Lee, Sejin Kim, Sundong Kim:
Solution Augmentation for ARC Problems Using GFlowNet: A Probabilistic Exploration Approach. - Nina Wiedemann, Sainan Liu, Quentin Leboutet, Katelyn Gao, Benjamin Ummenhofer, Michael Paulitsch, Kai Yuan:
Unifi3D: A Study on 3D Representations for Generation and Reconstruction in a Common Framework. - Laura Perez-Beltrachini, Mirella Lapata:
Uncertainty Quantification in Retrieval Augmented Question Answering. - Kobi Rahimi, Yehonathan Refael, Tom Tirer, Ofir Lindenbaum:
Unveiling Multiple Descents in Unsupervised Autoencoders. - Chi Han, Ziqi Wang, Han Zhao, Heng Ji:
Understanding Emergent In-Context Learning from a Kernel Regression Perspective. - Huaiyuan Qin, Muli Yang, Siyuan Hu, Peng Hu, Yu Zhang, Chen Gong, Hongyuan Zhu:
Beyond Instance Consistency: Investigating View Diversity in Self-supervised Learning. - Shruti P. Maralappanavar, Bettagere Nagaraja Bharath:
Byzantine-Robust and Hessian-Free Federated Bilevel Optimization. - Emmanouil Angelis, Francesco Quinzan, Ashkan Soleymani, Patrick Jaillet, Stefan Bauer:
Double Machine Learning Based Structure Identification from Temporal Data. - Jinendra Malekar, Ramtin Zand:
Amdahl's Law for LLMs: A Throughput-Centric Analysis of Extreme LLM Quantization. - Christian P. C. Franssen, Jinyang Jiang, Yijie Peng, Bernd Heidergott:
CoNNect: Connectivity-Based Regularization for Structural Pruning of Neural Networks. - Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Curtis Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal:
Low Compute Unlearning via Sparse Representations. - Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda, André Hottung, Niels A. Wouda, Leon Lan, Junyoung Park, Kevin Tierney, Jinkyoo Park:
RouteFinder: Towards Foundation Models for Vehicle Routing Problems. - Daniel Cortild, Claire Delplancke, Nadia Oudjane, Juan Peypouquet:
Global Optimization Algorithm through High-Resolution Sampling. - Florian Peter Busch, Moritz Willig, Matej Zecevic, Kristian Kersting, Devendra Singh Dhami:
Structural Causal Circuits: Probabilistic Circuits Climbing All Rungs of Pearl's Ladder of Causation. - Hiroki Naganuma, Xinzhi Zhang, Man-Chung Yue, Ioannis Mitliagkas, Russell J. Hewett, Philipp A. Witte, Yin Tat Lee:
Pseudo-Asynchronous Local SGD: Robust and Efficient Data-Parallel Training. - Kamil Ciosek, Nicolò Felicioni, Sina Ghiassian:
Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy. - Stefano Bruno, Sotirios Sabanis:
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients. - Mo Li, Songyang Zhang, Taolin Zhang, Haodong Duan, Yunxin Liu, Kai Chen:
NeedleBench: Evaluating LLM Retrieval and Reasoning Across Varying Information Densities. - Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt:
Two Is Better Than One: Aligned Representation Pairs for Anomaly Detection. - Aapo Hyvärinen:
A noise-corrected Langevin algorithm and sampling by half-denoising. - Xuyi Meng, Chen Wang, Jiahui Lei, Kostas Daniilidis, Jiatao Gu, Lingjie Liu:
Zero-1-to-G: Taming Pretrained 2D Diffusion Model for Direct 3D Generation. - Mostafa ElAraby, Sabyasachi Sahoo, Yann Pequignot, Paul Novello, Liam Paull:
GROOD: GRadient-Aware Out-of-Distribution Detection. - Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig:
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. - Vedant Dave, Ozan Özdenizci, Elmar Rueckert:
Learning Robust Representations for Visual Reinforcement Learning via Task-Relevant Mask Sampling. - Amir Ali Farzin, Yuen-Man Pun, Philipp Braun, Antoine Lesage-Landry, Youssef Diouane, Iman Shames:
Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extragradient Algorithm. - Shyam Venkatasubramanian, Sean Moushegian, Ahmed Aloui, Vahid Tarokh:
An Information-Theoretic Lower Bound on the Generalization Error of Autoencoders. - Siddharth Jain, Shyamgopal Karthik, Vineet Gandhi:
Simplifying Knowledge Transfer in Pretrained Models. - Dario Serez, Marco Cristani, Alessio Del Bue, Vittorio Murino, Pietro Morerio:
A Mutual Information Perspective on Multiple Latent Variable Generative Models for Positive View Generation. - Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeffrey Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Isaac Bloom, Stella Biderman, Adrià Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, William Saunders, Eric J. Michaud, Stephen Casper, Max Tegmark, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Tom McGrath:
Open Problems in Mechanistic Interpretability. - Dominic Phillips, Flaviu Cipcigan:
MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNets. - Timo Lüddecke, Alexander S. Ecker:
Characterizing Vision Backbones for Dense Prediction with Dense Attentive Probing. - Boyang Zhang, Quanqi Hu, Mingxuan Sun, Qihang Lin, Tianbao Yang:
Learning to Rank with Top-$K$ Fairness. - Xingshuai Huang, Di Wu, Benoit Boulet:
DRDT3: Diffusion-Refined Decision Test-Time Training Model. - Mihnea Ghitu, Vihari Piratla, Matthew Wicker:
Model Guidance via Robust Feature Attribution. - Boyi Li, Ligeng Zhu, Ran Tian, Shuhan Tan, Yuxiao Chen, Yao Lu, Yin Cui, Sushant Veer, Max Ehrlich, Jonah Philion, Xinshuo Weng, Fuzhao Xue, Linxi Fan, Yuke Zhu, Jan Kautz, Andrew Tao, Ming-Yu Liu, Sanja Fidler, Boris Ivanovic, Trevor Darrell, Jitendra Malik, Song Han, Marco Pavone:
Wolf: Dense Video Captioning with a World Summarization Framework. - Jingming Liu, Yumeng Li, Boyuan Xiao, Yichang Jian, Ziang Qin, Tianjia Shao, Yao-Xiang Ding, Kun Zhou:
Autonomous Imagination: Closed-Loop Decomposition of Visual-to-Textual Conversion in Visual Reasoning for Multimodal Large Language Models. - Yimu Wang, Xuye Liu, Wei Pang, Li Ma, Shuai Yuan, Paul E. Debevec, Ning Yu:
Survey of Video Diffusion Models: Foundations, Implementations, and Applications. - Mohammadreza Nemati, Zhipeng Huang, Kevin S. Xu:
LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models. - Abhinav Anand, Subrahmanya Swamy Peruru, Amitangshu Pal:
VColRL: Learn to solve the Vertex Coloring Problem using Reinforcement Learning. - Alexandre Piché, Aristides Milios, Dzmitry Bahdanau, Christopher Pal:
LLMs can learn self-restraint through iterative self-reflection. - Shayan Alahyari, Mike Domaratzki:
Local Distribution-Based Adaptive Oversampling for Imbalanced Regression. - Alejandro Gomez-Leos, Oscar López:
Simple and Nearly-Optimal Sampling for Rank-1 Tensor Completion via Gauss-Jordan. - Mengxi Wu, Hao Huang, Yi Fang, Mohammad Rostami:
Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud. - John Martinsson, Tuomas Virtanen, Maria Sandsten, Olof Mogren:
The Accuracy Cost of Weakness: A Theoretical Analysis of Fixed-Segment Weak Labeling for Events in Time. - Franz A. Heinsen, Leo Kozachkov:
Generalized Orders of Magnitude for Scalable, Parallel, High-Dynamic-Range Computation. - Jan Wehner, Sahar Abdelnabi, Daniel Tan, David Krueger, Mario Fritz:
Taxonomy, Opportunities, and Challenges of Representation Engineering for Large Language Models. - Delaram Pirhayatifard, Arlei Silva:
Cross-Domain Graph Anomaly Detection via Test-Time Training with Homophily-Guided Self-Supervision. - Yueqin Yin, Shentao Yang, Yujia Xie, Ziyi Yang, Yuting Sun, Hany Hassan Awadalla, Weizhu Chen, Mingyuan Zhou:
Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model. - Onur Boyar, Hiroyuki Hanada, Ichiro Takeuchi:
Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design. - Yupeng Chen, Senmiao Wang, Yushun Zhang, Zhihang Lin, Haozhe Zhang, Weijian Sun, Tian Ding, Ruoyu Sun:
MoFO: Momentum-Filtered Optimizer for Mitigating Forgetting in LLM Fine-Tuning. - Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du, Tianyu Pang, Wei Gao, Min Lin:
LightTransfer: Your Long-Context LLM is Secretly a Hybrid Model with Effortless Adaptation. - Anwesha Banerjee, Soma Biswas:
Language-assisted Feature Representation and Lightweight Active Learning For On-the-Fly Category Discovery. - F. William Townes:
Exponential tilting of subweibull distributions. - Benjamin Thérien, Charles-Étienne Joseph, Zain Sarwar, Ashwinee Panda, Anirban Das, Shi-Xiong Zhang, Stephen Rawls, Sambit Sahu, Eugene Belilovsky, Irina Rish:
Continual Pre-training of MoEs: How robust is your router? - Amir M. Mansourian, Rozhan Ahmadi, Masoud Ghafouri, Amir Mohammad Babaei, Elaheh Badali Golezani, Zeynab Yasamani Ghamchi, Vida Ramezanian, Alireza Taherian, Kimia Dinashi, Amirali Miri, Shohreh Kasaei:
A Comprehensive Survey on Knowledge Distillation. - Pascal Plettenberg, Dominik Köhler, Bernhard Sick, Josephine M. Thomas:
Flow-Attentional Graph Neural Networks. - Yuchen Shen, Haomin Wen, Leman Akoglu:
FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection. - Robert Kasumba, Guanghui Yu, Chien-Ju Ho, Sarah Keren, William Yeoh:
Goal Recognition Design for General Behavioral Agents using Machine Learning. - Sicheng Feng, Gongfan Fang, Xinyin Ma, Xinchao Wang:
Efficient Reasoning Models: A Survey. - Nuojin Cheng, Alireza Doostan, Stephen Becker:
Stochastic Subspace Descent Accelerated via Bi-fidelity Line Search. - Shion Takeno, Yu Inatsu, Masayuki Karasuyama, Ichiro Takeuchi:
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization. - Agil Aghasanli, Plamen P. Angelov:
Recursive SNE: Fast Prototype-Based t-SNE for Large-Scale and Online Data. - Yanxi Chen, Yaliang Li, Bolin Ding, Jingren Zhou:
Designing Algorithms Empowered by Language Models: An Analytical Framework, Case Studies, and Insights. - Armando J. Cabrera Pacheco, Rabanus Derr, Robert C. Williamson:
Aggregating Algorithm and Axiomatic Loss Aggregation. - Beyza Kalkanli, Tales Imbiriba, Stratis Ioannidis, Deniz Erdogmus, Jennifer G. Dy:
Dependency-aware Maximum Likelihood Estimation for Active Learning. - Xiwei Cheng, Xiangxin Zhou, Yuwei Yang, Yu Bao, Quanquan Gu:
Decomposed Direct Preference Optimization for Structure-Based Drug Design. - Mohsen Sadr, Tony Tohme, Kamal Youcef-Toumi:
Data-Driven Discovery of PDEs via the Adjoint Method. - Zhanhong Jiang, Md. Zahid Hasan, Nastaran Saadati, Aditya Balu, Chao Liu, Soumik Sarkar:
Balancing Utility and Privacy: Dynamically Private SGD with Random Projection. - Moein Sorkhei, Christos Matsoukas, Johan Fredin Haslum, Emir Konuk, Kevin Smith:
k-NN as a Simple and Effective Estimator of Transferability. - Padmaksha Roy, Almuatazbellah Boker, Lamine Mili:
Beyond Marginals: Learning Joint Spatio-Temporal Patterns for Multivariate Anomaly Detection. - Daniil Vankov, Angelia Nedich, Lalitha Sankar:
Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence Rates. - Asher Labovich:
A Case for Library-Level $k$-Means Binning in Histogram Gradient-Boosted Trees. - Jang-Hyun Kim, Claudia Skok Gibbs, Sangdoo Yun, Hyun Oh Song, Kyunghyun Cho:
Large-Scale Targeted Cause Discovery via Learning from Simulated Data. - Saumyaranjan Mohanty, Konda Reddy Mopuri:
Coreset-Driven Re-Labeling: Tackling Noisy Annotations with Noise-Free Gradients. - Pau Vilimelis Aceituno, Jack William Miller, Noah Marti, Youssef Farag, Victor Boussange:
Temporal horizons in forecasting: a performance-learnability trade-off. - Noam Elata, Tomer Michaeli, Michael Elad:
PSC: Posterior Sampling-Based Compression. - Andi Nika, Sepehr Elahi, Cem Tekin:
Contextual Combinatorial Bandits With Changing Action Sets Via Gaussian Processes. - Saptarshi Mandal, Seo Taek Kong, Dimitrios Katselis, R. Srikant:
Spectral Clustering and Labeling for Crowdsourcing with Inherently Distinct Task Types. - Faissal El Kayouhi, Aïda Asma, Joey Laarhoven, Fiona Nagelhout:
Revisiting B2T: Discovering and Mitigating Visual Biases through Keyword Explanations. - Cheng Chen, Atsushi Nitanda, Ivor W. Tsang:
Unlearning Misalignment for Personalized LLM Adaptation via Instance-Response-Dependent Discrepancies. - Inês Cardoso Oliveira, Decebal Constantin Mocanu, Luis A. Leiva:
Sparse-to-Sparse Training of Diffusion Models. - Yuxuan Bai, Gauri Pradhan, Marlon Tobaben, Antti Honkela:
Empirical Comparison of Membership Inference Attacks in Deep Transfer Learning. - Frank Shih, Faming Liang:
Latent Trajectory: A New Framework for Deep Actor-Critic Reinforcement Learning with Uncertainty Quantification. - James Matthew Young, Ömer Deniz Akyildiz:
On diffusion posterior sampling via sequential Monte Carlo for zero-shot scaffolding of protein motifs. - Lasse Elsemüller, Valentin Pratz, Mischa von Krause, Andreas Voss, Paul-Christian Bürkner, Stefan T. Radev:
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation. - Emanuele Ballarin, Alessio Ansuini, Luca Bortolussi:
Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness. - Miles Everett, Aiden Durrant, Mingjun Zhong, Georgios Leontidis:
Capsule Network Projectors are Equivariant and Invariant Learners. - Philipp Foth, Lukas Gosch, Simon Geisler, Leo Schwinn, Stephan Günnemann:
Adversarial Robustness of Graph Transformers. - Anh-Tien Nguyen, Duy Minh Ho Nguyen, Nghiem Tuong Diep, Trung Quoc Nguyen, Nhat Ho, Jacqueline Michelle Metsch, Miriam Cindy Maurer, Daniel Sonntag, Hanibal Bohnenberger, Anne-Christin Hauschild:
MGPATH: A Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot Whole Slide Pathology Classification. - Xinyu Yang, Junlin Han, Rishi Bommasani, Jinqi Luo, Wenjie Qu, Wangchunshu Zhou, Adel Bibi, Xiyao Wang, Jaehong Yoon, Elias Stengel-Eskin, Shengbang Tong, Lingfeng Shen, Rafael Rafailov, Runjia Li, Zhaoyang Wang, Yiyang Zhou, Chenhang Cui, Yu Wang, Wenhao Zheng, Huichi Zhou, Jindong Gu, Zhaorun Chen, Peng Xia, Tony Lee, Thomas P. Zollo, Vikash Sehwag, Jixuan Leng, Jiuhai Chen, Yuxin Wen, Huan Zhang, Zhun Deng, Linjun Zhang, Pavel Izmailov, Pang Wei Koh, Yulia Tsvetkov, Andrew Gordon Wilson, Jiaheng Zhang, James Zou, Cihang Xie, Hao Wang, Philip Torr, Julian J. McAuley, David Alvarez-Melis, Florian Tramèr, Kaidi Xu, Suman Jana, Chris Callison-Burch, René Vidal, Filippos Kokkinos, Mohit Bansal, Beidi Chen, Huaxiu Yao:
Reliable and Responsible Foundation Models. - Xingzi Xu, Amir Tavanaei, Kavosh Asadi, Karim Bouyarmane:
Activation sharding for scalable training of large models. - Amin Jalali, Milad Soltany, Michael A. Greenspan, Ali Etemad:
Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing. - Sihyeon Kim, Boryeong Cho, Sangmin Bae, Sumyeong Ahn, Se-Young Yun:
VSCoDe: Visual-Augmentation Selection for Contrastive Decoding. - Zhengran Ji, Boyuan Chen:
Pref-GUIDE: Continual Policy Learning from Real-Time Human Feedback via Preference-Based Learning. - Peter Van Katwyk, Karianne Bergen:
HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model. - Lunjun Zhang, Shuo Han, Hanrui Lyu, Bradly C. Stadie:
D2 Actor Critic: Diffusion Actor Meets Distributional Critic. - Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang, Doina Precup:
Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations. - Jingcheng Niu, Subhabrata Dutta, Ahmed Elshabrawy, Harish Tayyar Madabushi, Iryna Gurevych:
Illusion or Algorithm? Investigating Memorization, Emergence, and Symbolic Processing in In-Context Learning. - Linli Zhou, Bokun Wang, My T. Thai, Tianbao Yang:
Stochastic Primal-Dual Double Block-Coordinate for Two- way Partial AUC Maximization. - Aakash Lahoti, Tanya Marwah, Ratish Puduppully, Albert Gu:
Chimera: State Space Models Beyond Sequences. - Gaurav Chaudhary, Laxmidhar Behera:
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning. - Rajni Dabas, Neelima Gupta, Rudra Bhardwaj, Sapna Grover:
EL-Clustering: Combining Upper- and Lower-Bounded Clusterings for Equitable Load Constraints. - Vasilis Siomos, Jonathan Passerat-Palmbach, Giacomo Tarroni:
An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration. - Saber Malekmohammadi, Afaf Taïk, Golnoosh Farnadi:
Differentially Private Clustered Federated Learning. - Joy Mahapatra, Soumyajit Roy, Utpal Garain:
Exponential Scaling of Factual Inconsistency in Data-to-Text Generation with Fine-Tuned LLMs. - Lea Demelius, Simone Kopeinik, Dominik Kowald, Roman Kern, Andreas Trügler:
Private and Fair Machine Learning: Revisiting the Disparate Impact of Differentially Private SGD. - Erfan Hajihashemi, Yanning Shen:
Multi-model Online Conformal Prediction with Graph-Structured Feedback. - Kirill Bykov, Marina M.-C. Höhne, Adelaida Creosteanu, Klaus-Robert Müller, Frederick Klauschen, Shinichi Nakajima, Marius Kloft:
Explaining Bayesian Neural Networks. - Rashed Shelim, Shengzhe Xu, Walid Saad, Naren Ramakrishnan:
Is isotropy a good proxy for generalization in time series forecasting with transformers? - Byeongchan Lee:
Understanding Self-supervised Contrastive Learning through Supervised Objectives. - Gaoqin Chang, Jun Shu, Xiang Yuan, Deyu Meng:
Diversity-Enhanced and Classification-Aware Prompt Learning for Few-Shot Learning via Stable Diffusion. - Mélanie Roschewitz, Raghav Mehta, Fabio De Sousa Ribeiro, Ben Glocker:
Where are we with calibration under dataset shift in image classification? - James Robert Golden:
Equivalent Linear Mappings of Large Language Models. - Kin Kwan Leung, Rasa Hosseinzadeh, Gabriel Loaiza-Ganem:
On Convolutions, Intrinsic Dimension, and Diffusion Models. - Tianyi Xiang, Weiying Zheng, Yutao Jiang, Tingrui Shen, Hewei Yu, Yangyang Xu, Shengfeng He:
Teaching Diffusion Models to Ground Alpha Matte. - Zhiheng Lyu, Xueguang Ma, Wenhu Chen:
PixelWorld: Towards Perceiving Everything as Pixels. - Gang Qiao, Ambuj Tewari:
An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem. - Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf, David Krueger, Andrew Paverd, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Menglin Xia, Santiago Zanella-Béguelin:
Permissive Information-Flow Analysis for Large Language Models. - Yifan Zhang, Chen Huang, Yueke Zhang, Huajie Shao, Kevin Leach, Yu Huang:
Pre-Training Representations of Binary Code Using Contrastive Learning. - Chi-Wei Chang, Richard Tzong-Han Tsai:
FORTRESS: Fast, Tuning-Free Retrieval Ensemble for Scalable LLM Safety. - Atsuki Yamaguchi, Terufumi Morishita, Aline Villavicencio, Nikolaos Aletras:
Adapting Chat Language Models Using Only Target Unlabeled Language Data. - Xiaoyang Wang, Yibo Jacky Zhang, Olawale Elijah Salaudeen, Mingyuan Wu, Hongpeng Guo, Chaoyang He, Klara Nahrstedt, Sanmi Koyejo:
Improving Single-round Active Adaptation: A Prediction Variability Perspective. - Semih Cayci, Atilla Eryilmaz:
Recurrent Natural Policy Gradient for POMDPs. - Montaser Mohammedalamen, Dustin Morrill, Alexander Sieusahai, Yash Satsangi, Michael Bowling:
Learning to Be Cautious. - Benedikt Alkin, Maurits Bleeker, Richard Kurle, Tobias Kronlachner, Reinhard Sonnleitner, Matthias Dorfer, Johannes Brandstetter:
AB-UPT: Scaling Neural CFD Surrogates for High- Fidelity Automotive Aerodynamics Simulations via Anchored- Branched Universal Physics Transformers. - Difan Deng, Marius Lindauer:
Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach. - Jiacheng Lin, Tian Wang, Kun Qian:
Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning. - Tai Le Gia, Jaehyun Ahn:
On the Problem of Consistent Anomalies in Zero-Shot Industrial Anomaly Detection. - David P. Hofmeyr:
Bags of Projected Nearest Neighbours: Competitors to Random Forests? - João B. S. Carvalho, Victor Jimenez Rodriguez, Alessandro Torcinovich, Antonio Emanuele Cinà, Carlos Cotrini, Lea Schönherr, Joachim M. Buhmann:
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach. - Aurélien Garivier, Emmanuel Pilliat:
On Sparsity and Sub-Gaussianity in the Johnson- Lindenstrauss Lemma. - Andrey Bochkov:
Emergent Semantics Beyond Token Embeddings: Transformer LMs with Frozen Visual Unicode Representations. - Hoyoung Kim, Seokhee Jin, Changhwan Sung, Jaechang Kim, Jungseul Ok:
Active Prompt Learning with Vision-Language Model Priors. - Amir Saeidi, Yiran Lawrence Luo, Agneet Chatterjee, Shamanthak Hegde, Bimsara Pathiraja, Yezhou Yang, Chitta Baral:
Dual Caption Preference Optimization for Diffusion Models. - Mona Schirmer, Dan Zhang, Eric T. Nalisnick:
Temporal Test-Time Adaptation with State-Space Models. - Zimu Lu, Aojun Zhou, Ke Wang, Houxing Ren, Weikang Shi, Yunqiao Yang, Junting Pan, Mingjie Zhan, Hongsheng Li:
Step-Controlled DPO: Leveraging Stepwise Errors for Enhancing Mathematical Reasoning of Language Models. - Jiazhi Li, Mi Zhou, Mahyar Khayatkhoei, Jingyu Shi, Xiang Gao, Jiageng Zhu, Hanchen Xie, Xiyun Song, Zongfang Lin, Heather Yu, Jieyu Zhao:
Enhancing Diversity in Text-to-Image Generation without Compromising Fidelity. - Mohamad Louai Shehab, Antoine Aspeel, Necmiye Ozay:
Learning Reward Machines from Partially Observed Policies. - Marius Huber, Sara Kalisnik Hintz, Patrick Schnider:
AuToMATo: An Out-Of-The-Box Persistence-Based Clustering Algorithm. - Xin Jin, Yichuan Zhong, Yapeng Tian:
TP‑Blend: Textual‑Prompt Attention Pairing for Precise Object‑Style Blending in Diffusion Models. - Harsh Vardhan, Arya Mazumdar:
Collaborative Compressors in Distributed Mean Estimation with Limited Communication Budget. - Aakash Kumar Singh, Priyam Dey, Sribhav Srivatsa, Venkatesh Babu Radhakrishnan:
Concept Siever : Towards Controllable Erasure of Concepts from Diffusion Models without Side-effect. - Hanwen Xing, Christopher Yau:
Continual learning via probabilistic exchangeable sequence modelling. - Jung-Hun Kim, Se-Young Yun:
Adversarial Bandits Against Arbitrary Strategies. - Christian Raymond, Qi Chen, Bing Xue, Mengjie Zhang:
Meta-Learning Adaptive Loss Functions. - Haozhen Zhang, Tao Feng, Pengrui Han, Jiaxuan You:
AcademicEval: Live Long-Context LLM Benchmark. - Shengkun Tang, Liqun Ma, Haonan Li, Mingjie Sun, Zhiqiang Shen:
Bi-Mamba: Towards Accurate 1-Bit State Space Model. - Simon Rittel, Sebastian Tschiatschek:
Expressiveness of Parametrized Distributions over DAGs for Causal Discovery. - Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou:
Improved Localized Machine Unlearning Through the Lens of Memorization. - Ryan Y. Lin, Julius Berner, Valentin Duruisseaux, David Pitt, Daniel V. Leibovici, Jean Kossaifi, Kamyar Azizzadenesheli, Anima Anandkumar:
Enabling Automatic Differentiation with Mollified Graph Neural Operators. - Dániel Rácz, Mihály Petreczky, Bálint Daróczy:
Length independent generalization bounds for deep SSM architectures via Rademacher contraction and stability constraints. - Irene Testini, Lorenzo Pacchiardi, José Hernández-Orallo:
Measuring Data Science Automation: A Survey of Evaluation Tools for AI Assistants and Agents. - Shivani Tomar, Seshu Tirupathi, Radu Marinescu, Elizabeth M. Daly, Ivana Dusparic:
AT4TS : Autotune for Time Series Foundation Models. - Hung Le, Van Dai Do, Dung Nguyen, Svetha Venkatesh:
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language Models. - Weitang Liu, Yuelei Li, Ying Wai Li, Zihan Wang, Yi-Zhuang You, Jingbo Shang:
OmniInput: An Evaluation Framework for Deep Learning Models on Internet-Scale Data. - Zahraa Al Sahili, Ioannis Patras, Matthew Purver:
Data Matters Most: Auditing Social Bias in Contrastive Vision-Language Models. - Yuan Gao, Yuki Takezawa, Sebastian U. Stich:
A Bias Correction Mechanism for Distributed Asynchronous Optimization. - Anas Jnini, Flavio Vella:
Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks. - Hang Lv, Pengxiang Zhan, Yanchao Tan, Zixuan Guo, Shiping Wang, Carl Yang:
GMAgent: A Graph-oriented Multi-agent Collaboration Framework for Text-attributed Graph Analysis. - Zicong Zhu, Issei Sato:
Prior Specification for Exposure-based Bayesian Matrix Factorization. - Stephen Pasteris, Madeleine Dwyer, Chris Hicks, Vasilios Mavroudis:
A Hierarchical Nearest Neighbour Approach to Contextual Bandits. - Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Christopher J. Cueva, Erin Grant, Iris I. A. Groen, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nathan Cloos, Nikolaus Kriegeskorte, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew Kyle Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths:
Getting aligned on representational alignment. - Ryoma Sato:
Solving the Cold Start Problem on One's Own as an End User via Preference Transfer. - Dongheng Lin, Han Hu, Jianbo Jiao:
What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images. - Fabian Schaipp, Guillaume Garrigos, Umut Simsekli, Robert M. Gower:
Tracking the Median of Gradients with a Stochastic Proximal Point Method. - Azwar Abdulsalam, Joseph G. Makin:
Revisiting Contrastive Divergence for Density Estimation and Sample Generation. - Deep Kumar Ganguly, Ajin George Joseph, Sarthak Girotra, Sirish Sekhar:
Risk‑Seeking Reinforcement Learning via Multi‑Timescale EVaR Optimization. - Natalie Frank:
Adversarial Surrogate Risk Bounds for Binary Classification. - Parthiv Chatterjee, Shivam Sonawane, Amey Hengle, Aditya Tanna, Sourish Dasgupta, Tanmoy Chakraborty:
Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers. - Guillaume Carrière, Frédéric Cazals:
Improved seeding strategies for k-means and k-GMM. - Fabian Akkerman, Julien Ferry, Christian Artigues, Emmanuel Hebrard, Thibaut Vidal:
Boosting Revisited: Benchmarking and Advancing LP-Based Ensemble Methods. - Luoxiao Yang, Yun Wang, Xinqi Fan, Israel Cohen, Jingdong Chen, Zijun Zhang:
ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence. - Mathurin Videau, Alessandro Ferreira Leite, Marc Schoenauer, Olivier Teytaud:
Mixture of Experts for Image Classification: What's the Sweet Spot? - Zifu Wang, Junyi Zhu, Bo Tang, Zhiyu Li, Feiyu Xiong, Jiaqian Yu, Matthew B. Blaschko:
Jigsaw-R1: A Study of Rule-based Visual Reinforcement Learning with Jigsaw Puzzles. - Candi Zheng, Yuan Lan, Yang Wang:
LanPaint: Training-Free Diffusion Inpainting with Asymptotically Exact and Fast Conditional Sampling. - Divyansh Jhunjhunwala, Pranay Sharma, Zheng Xu, Gauri Joshi:
Initialization Matters: Unraveling the Impact of Pre-Training on Federated Learning. - Haoxiang Zhang, Zhuofeng Li, Qiannan Zhang, Ziyi Kou, Juncheng Li, Shichao Pei:
Avoiding Structural Pitfalls: Self-Supervised Low-Rank Feature Tuning for Graph Test-Time Adaptation. - Sonal Allana, Mohan Kankanhalli, Rozita Dara:
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review. - Nicolas Talabot, Olivier Clerc, Arda Cinar Demirtas, Hieu Le, Doruk Öner, Pascal Fua:
PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization. - Gal Maman, Ronen Talmon:
Geometric Optimal Transport for Unsupervised Domain Adaptation. - Fatemeh Ghofrani, Mehdi Yaghouti, Pooyan Jamshidi:
An Empirical Study of the Accuracy-Robustness Trade-off and Training Efficiency in Robust Self-Supervised Learning. - Amir Aghdam, Vincent Tao Hu, Björn Ommer:
ActAlign: Zero-Shot Fine-Grained Video Classification via Language-Guided Sequence Alignment. - Gabriel Mongaras, Eric C. Larson:
On the Expressiveness of Softmax Attention: A Recurrent Neural Network Perspective. - Sayantan Das, Mojtaba Kolahdouzi, Ali Etemad:
Kernel Space Conditional Distribution Alignment for Improving Group Fairness in Deepfake Detection. - Tianwei Ni, Allen Nie, Sapana Chaudhary, Yao Liu, Huzefa Rangwala, Rasool Fakoor:
Offline Learning and Forgetting for Reasoning with Large Language Models. - Laurits Fredsgaard, Mikkel N. Schmidt:
On Joint Regularization and Calibration in Deep Ensembles. - Mohamed Aboelenien Ahmed, Kilian Pfeiffer, Osama Abboud, Ramin Khalili, Heba Khdr, Jörg Henkel:
Accelerated Training on Low-Power Edge Devices. - Yaozhong Shi, Zachary E. Ross, Domniki Asimaki, Kamyar Azizzadenesheli:
Mesh-Informed Neural Operator : A Transformer Generative Approach. - Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg, Emma Strubell, Michael Oberst, Bryan Wilder, Zachary Chase Lipton:
Expert Routing with Synthetic Data for Domain Incremental Learning. - Cristian Perez Jensen, Seyedmorteza Sadat:
Efficient Distillation of Classifier-Free Guidance using Adapters. - Vasudevan Nedumpozhimana, John D. Kelleher:
Know Yourself and Know Your Neighbour : A Syntactically Informed Self-Supervised Compositional Sentence Representation Learning Framework using a Recursive Hypernetwork. - Md Kaykobad Reza, Ameya Patil, Mashhour Solh, M. Salman Asif:
Robust Multimodal Learning via Cross-Modal Proxy Tokens. - Madhusudan Verma, Manoj Kumar:
Analysis of generalization capacities of Neural Ordinary Differential Equations. - Yeshwanth Venkatesha, Souvik Kundu, Priyadarshini Panda:
Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection. - Peyman Gholami, Hulya Seferoglu:
Differentiated Aggregation to Improve Generalization in Federated Learning. - Chao Han, Stefanos Ioannou, Luca Manneschi, Thomas J. Hayward, Michael Mangan, Aditya Gilra, Eleni Vasilaki:
Neural ODE and SDE Models for Adaptation and Planning in Model-Based Reinforcement Learning. - Fengzhe Zhang, Laurence Illing Midgley, José Miguel Hernández-Lobato:
Efficient and Unbiased Sampling from Boltzmann Distributions via Variance-Tuned Diffusion Models. - Hassan Baker, Matthew Emigh, Austin J. Brockmeier:
Weakly Supervised Object Segmentation by Background Conditional Divergence. - Quang Phuoc Minh Pham, Nguyen Tiet Nguyen Khoi, Lan Chi Ngo, Do Tho Truong, Dezhen Song, Truong-Son Hy:
TESGNN: Temporal Equivariant Scene Graph Neural Networks for Efficient and Robust Multi-View 3D Scene Understanding. - Siyuan Chen, Fuyuan Zhang, Zhuo Li, Xiongfei Wu, Jianlang Chen, Pengzhan Zhao, Lei Ma, Jianjun Zhao:
Improving Adversarial Training for Two-player Competitive Games via Episodic Reward Engineering. - Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu:
Identification of Average Outcome under Interventions in Confounded Additive Noise Models. - Yanhao Jin, Krishna Balasubramanian, Lifeng Lai:
In-context Learning for Mixture of Linear Regression: Existence, Generalization and Training Dynamics. - Alexander Becker, Rodrigo Caye Daudt, Dominik Narnhofer, Torben Peters, Nando Metzger, Jan Dirk Wegner, Konrad Schindler:
Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields. - Cameron Ethan Taylor, Shreyas Malakarjun Patil, Constantine Dovrolis:
Before Forgetting, There's Learning: Representation Learning Challenges in Online Unsupervised Continual Learning. - Jingxuan Zhu, Bin Liu:
Thompson Sampling For Bandits With Cool-Down Periods. - Khadichabonu Valieva, Bikramjit Banerjee:
Quasipseudometric Value Functions with Dense Rewards. - Martijn P. van Leeuwen, Koen V. Haak, Gorkem Saygili, Eric O. Postma, Lee-Ling Sharon Ong:
A Note On The Stability Of The Focal Loss. - Baohe Zhang, Yuan Zhang, Hao Zhu, Shengchao Yan, Thomas Brox, Joschka Boedecker:
Constrained Reinforcement Learning with Smoothed Log Barrier Function. - Ethan Ewer, Daewon Chae, Thomas Zeng, Jinkyu Kim, Kangwook Lee:
Encoder-only Next Token Prediction. - Sumantrak Mukherjee, Mouad Elhamdi, George Mohler, David Antony Selby, Yao Xie, Sebastian Josef Vollmer, Gerrit Großmann:
Neural Spatiotemporal Point Processes: Trends and Challenges. - GVS Mothish, J. Rishi, Shobhit Kumar Shukla, Deepak Subramani:
DNOD: Deformable Neural Operators for Object Detection in SAR Images. - Atsuki Sato, Yusuke Matsui:
PCF Learned Sort: a Learning Augmented Sort Algorithm with $\mathcal{O}(n \log\log n)$ Expected Complexity. - Guoxuan Xia, Harleen Hanspal, Petru-Daniel Tudosiu, Shifeng Zhang, Sarah Parisot:
A Practical Investigation of Spatially-Controlled Image Generation with Transformers. - Yao Xiao, Qiqian Fu, Heyi Tao, Yuqun Wu, Zhen Zhu, Derek Hoiem:
TextRegion: Text-Aligned Region Tokens from Frozen Image-Text Models. - Shivam Garg, Chirag Pabbaraju, Kirankumar Shiragur, Gregory Valiant:
Testing with Non-identically Distributed Samples. - Richard Williams, Eric T. Nalisnick, Andrew Holbrook:
Scalable Generative Modeling of Weighted Graphs. - Tianshi Zheng, Yixiang Chen, Chengxi Li, Chunyang Li, Qing Zong, Haochen Shi, Baixuan Xu, Yangqiu Song, Ginny Y. Wong, Simon See:
The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning. - Byeongchan Lee, John Won, Seunghyun Lee, Jinwoo Shin:
CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection. - Md Joshem Uddin, Astrit Tola, Varin Singh Sikand, Cuneyt Gurcan Akcora, Baris Coskunuzer:
SCNode: Spatial and Contextual Coordinates for Graph Representation Learning. - Gianluca Monaci, Rafael S. Rezende, Romain Deffayet, Gabriela Csurka, Guillaume Bono, Hervé Déjean, Stéphane Clinchant, Christian Wolf:
RANa: Retrieval-Augmented Navigation. - Abdullah Akgül, Gulcin Baykal, Manuel Haussmann, Melih Kandemir:
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization. - Martin Genzel, Patrick Putzky, Pengfei Zhao, Sebastian Schulze, Mattes Mollenhauer, Robert Seidel, Stefan Dietzel, Thomas Wollmann:
Choose Your Model Size: Any Compression of Large Language Models Without Re-Computation. - Yatong Bai, Jonah Casebeer, Somayeh Sojoudi, Nicholas J. Bryan:
DRAGON: Distributional Rewards Optimize Diffusion Generative Models. - Yu Gu, Kai Zhang, Yuting Ning, Boyuan Zheng, Boyu Gou, Tianci Xue, Cheng Chang, Sanjari Srivastava, Yanan Xie, Peng Qi, Huan Sun, Yu Su:
Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents. - Alexander Bodard, Konstantinos A. Oikonomidis, Emanuel Laude, Panagiotis Patrinos:
The inexact power augmented Lagrangian method for constrained nonconvex optimization. - Pankaj Kumar, Subhankar Mishra:
Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics. - Minhyuk Seo, Seongwon Cho, Minjae Lee, Diganta Misra, Hyeonbeom Choi, Seon Joo Kim, Jonghyun Choi:
GenOL: Generating Diverse Examples for Name-only Online Learning. - Yinglun Xu, Gagandeep Singh:
Universal Black-Box Targeted Reward Poisoning Attack Against Online Deep Reinforcement Learning. - Shuqi Ke, Charlie Hou, Sewoong Oh, Giulia Fanti:
Characterizing the Training Dynamics of Private Fine-tuning with Langevin diffusion. - Francesco Silvestrin, Chengkun Li, Luigi Acerbi:
Stacking Variational Bayesian Monte Carlo. - Sakshi Arya:
Batched Nonparametric Bandits via k-Nearest Neighbor UCB. - Penghang Liu, Haibei Zhu, Eleonora Kreacic, Svitlana Vyetrenko:
Privacy-Aware Time Series Synthesis via Public Knowledge Distillation. - Mouad El Bouchattaoui, Myriam Tami, Benoit Lepetit, Paul-Henry Cournède:
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data. - Phoenix Neale Williams, Jessica Schrouff, Lea Goetz:
An Evolutionary Algorithm for Black-Box Adversarial Attack Against Explainable Methods. - Yinglun Xu, Tarun Suresh, Rohan Gumaste, David Zhu, Ruirui Li, Zhengyang Wang, Haoming Jiang, Xianfeng Tang, Qingyu Yin, Monica Xiao Cheng, Qi Zeng, Chao Zhang, Gagandeep Singh:
Two-Step Offline Preference-Based Reinforcement Learning on Explicitly Constrained Policies. - Patrik Joslin Kenfack, Ulrich Aïvodji, Samira Ebrahimi Kahou:
Adaptive Group Robust Ensemble Knowledge Distillation. - Huyu Wu, Duo Su, Junjie Hou, Guang Li:
Dataset Condensation with Color Compensation. - Payal Mohapatra, Lixu Wang, Qi Zhu:
Phase-driven Generalizable Representation Learning for Nonstationary Time Series Classification. - Matteo El Hariry, Antoine Richard, Ricard Marsal, Luis Felipe Wolf Batista, Matthieu Geist, Cédric Pradalier, Miguel A. Olivares-Méndez:
RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation. - Santtu Tikka, Juha Karvanen:
Monotone Missing Data: A Blessing and a Curse. - Matthew Pugh, Nick Harris, Corina Cîrstea, John C. Grundy:
Learning Is a Kan Extension. - Wei Ye, Prashant Khanduri, Jiangweizhi Peng, Feng Tian, Jun Gao, Jie Ding, Zhi-Li Zhang, Mingyi Hong:
Distributed Hierarchical Decomposition Framework for Multimodal Timeseries Prediction. - Fabian Schneider, Duc-Lam Duong, Matti Lassas, Maarten V. de Hoop, Tapio Helin:
An Unconditional Representation of the Conditional Score in Infinite Dimensional Linear Inverse Problems. - Anwesha Mohanty, Venkatesh Balavadhani Parthasarathy, Arsalan Shahid:
The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance. - Dylan Bouchard, Mohit Singh Chauhan:
Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers. - Hao Wang, Yu Liu, Daniel Biggs, Haoru Wang, Jiandong Yu, Ping Huang:
Learning Deformable Body Interactions With Adaptive Spatial Tokenization. - Barproda Halder, Faisal Hamman, Pasan Dissanayake, Qiuyi Zhang, Ilia Sucholutsky, Sanghamitra Dutta:
Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition. - Keyan Chen, Yile Li, Da Long, Zhitong Xu, Wei W. Xing, Jacob D. Hochhalter, Shandian Zhe:
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data. - Zheng Zhang:
Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning. - Fatemeh Daneshfar, Abdulhady Abas Abdullah, Moloud Abdar, Pietro Lio:
UMP-Net: Uncertainty-Aware Mixture of Prompts Network for Efficient Instruction Tuning. - Anand Ganesh, Babhrubahan Bose, Anand Rajagopalan:
Schauder Bases for $C[0, 1]$ Using ReLU, Softplus and Two Sigmoidal Functions. - Debadyuti Mukherjee, Chris Zhuang, Yingzhou Lu, Tianfan Fu, Ruqi Zhang:
Gradient GA: Gradient Genetic Algorithm For Drug Molecular Design. - Sayantan Adak, Somnath Banerjee, Rajarshi Mandal, Avik Halder, Sayan Layek, Rima Hazra, Animesh Mukherjee:
MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation. - Evelyn J. Mannix, Liam Hodgkinson, Howard D. Bondell:
Preserving Angles Improves Feature Distillation. - Evelyn J. Mannix, Liam Hodgkinson, Howard D. Bondell:
ComFe: An Interpretable Head for Vision Transformers. - Jiannan Yang, Veronika Thost, Tengfei Ma:
Self-Supervised Learning on Molecular Graphs: A Systematic Investigation of Masking Design. - Adib Karimi, Mohammad Mehdi Ebadzadeh:
RIZE: Adaptive Regularization for Imitation Learning. - Yinzhe Shen, Ömer Sahin Tas, Kaiwen Wang, Royden Wagner, Christoph Stiller:
Divide and Merge: Motion and Semantic Learning in End-to-End Autonomous Driving. - Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany:
Higher Order Transformers With Kronecker-Structured Attention. - Yazhou Zhang, Chunwang Zou, Bo Wang, Jing Qin, Prayag Tiwari:
Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection. - Aurélien Renault, Alexis Bondu, Antoine Cornuéjols, Vincent Lemaire:
Early Classification of Time Series: A Survey and Benchmark. - Jeffrey Wen, Rizwan Ahmad, Philip Schniter:
Minimax Multi-Target Conformal Prediction with Applications to Imaging Inverse Problems. - Hardy Chen, Haoqin Tu, Fali Wang, Hui Liu, Xianfeng Tang, Xinya Du, Yuyin Zhou, Cihang Xie:
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models. - Rishabh Ranjan, Mayank Vatsa, Richa Singh:
IndicFake Meets SAFARI-LLM: Unifying Semantic and Acoustic Intelligence for Multilingual Deepfake Detection. - Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca:
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training. - Ruichen Chen, Keith G. Mills, Di Niu:
FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers. - Wei Zhang, Guni Sharon:
AEAP: A Reinforcement Learning Actor Ensemble Algorithm with Adaptive Pruning. - Manish Nagaraj, Deepak Ravikumar, Kaushik Roy:
Coresets from Trajectories: Selecting Data via Correlation of Loss Differences. - Pedro Seber, Richard D. Braatz:
LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm. - Katharina Flügel, Charlotte Debus, Markus Götz, Achim Streit, Marie Weiel:
Scaling Laws of Distributed Random Forests. - Yu Yang, Pan Xu:
Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer. - Pavel Rytír, Ales Wodecki, Georgios Korpas, Jakub Marecek:
ExDBN: Learning Dynamic Bayesian Networks using Extended Mixed-Integer Programming Formulations. - Navodita Sharma, Vishnu Vinod, Abhradeep Guha Thakurta, Alekh Agarwal, Borja Balle, Christoph Dann, Aravindan Raghuveer:
Preserving Expert-Level Privacy in Offline Reinforcement Learning. - Zhongjie Dai, Tao Feng, Jiaxuan You:
PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modeling. - Jingwei Zhang, Xi Han, Hong Qin, Mahdi S. Hosseini, Dimitris Samaras:
LBMamba: Locally Bi-directional Mamba. - Charlesquin Kemajou Mbakam, Marcelo Pereyra, Jonathan Spence:
Learning few-step posterior samplers by unfolding and distillation of diffusion models. - Marek Herde, Lukas Lührs, Denis Huseljic, Bernhard Sick:
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels. - Yang yang, Xiaolu Zhou, Bosong Ding, Miao Xin:
Uncertainty-aware Reward Design Process. - Stephen Price, Elke A. Rundensteiner, Danielle L. Cote:
Mastering SAM Prompts: A Large-Scale Empirical Study in Segmentation Refinement for Scientific Imaging. - Zheyuan Liu, Junyan Wang, Zicheng Duan, Cristian Rodriguez Opazo, Anton van den Hengel:
Frame-wise Conditioning Adaptation for Fine-Tuning Diffusion Models in Text-to-Video Prediction. - Meng Ding, Rohan Sharma, Changyou Chen, Jinhui Xu, Kaiyi Ji:
Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective. - Chenyang Wu, Qian Chen, Akang Wang, Tian Ding, Ruoyu Sun, Wenguo Yang, Qingjiang Shi:
On Representing Convex Quadratically Constrained Quadratic Programs via Graph Neural Networks. - Guoyizhe Wei, Feng Wang, Anshul Shah, Rama Chellappa:
Learning to Prompt Your Domain for Federated Vision-Language Models. - Zhongyu Yang, Dannong Xu, Wei Pang, Yingfang Yuan:
Script: Graph-Structured and Query-Conditioned Semantic Token Pruning for Multimodal Large Language Models. - Yang Li, Daniel Agyei Asante, Changsheng Zhao, Ernie Chang, Yangyang Shi, Vikas Chandra:
Streamlining Language Models via Semantic Basis Analysis. - Sanjay Das, Swastik Bhattacharya, Souvik Kundu, Shamik Kundu, Anand Menon, Arnab Raha, Kanad Basu:
AttentionBreaker: Adaptive Evolutionary Optimization for Unmasking Vulnerabilities in LLMs through Bit-Flip Attacks. - Dong Li, Guihong Wan, Xintao Wu, Xinyu Wu, Xiaohui Chen, Yi He, Zhong Chen, Peter K. Sorger, Chen Zhao:
Multi-Modal Foundation Models for Computational Pathology: A Survey. - Jan Niklas Böhm, Marius Keute, Alica Guzmán, Sebastian Damrich, Andrew Draganov, Dmitry Kobak:
Node Embeddings via Neighbor Embeddings. - Massimiliano Ciranni, Luca Molinaro, Carlo Alberto Barbano, Attilio Fiandrotti, Vittorio Murino, Vito Paolo Pastore, Enzo Tartaglione:
Say My Name: a Model's Bias Discovery Framework. - Seongbeom Park, Hyunju Yun, Daewon Chae, Sungyoon Kim, Suhong Moon, Minwoo Kang, Seunghyun Park, Jinkyu Kim:
Hard-Negative Prototype-Based Regularization for Few-Shot Class-Incremental Learning. - Gian Maria Campedelli, Nicolò Penzo, Massimo Stefan, Roberto Dessì, Marco Guerini, Bruno Lepri, Jacopo Staiano:
I Want to Break Free! Persuasion and Anti-Social Behavior of LLMs in Multi-Agent Settings with Social Hierarchy. - Shuo Xing, Hongyuan Hua, Xiangbo Gao, Shenzhe Zhu, Renjie Li, Kexin Tian, Xiaopeng Li, Heng Huang, Tianbao Yang, Zhangyang Wang, Yang Zhou, Huaxiu Yao, Zhengzhong Tu:
AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving. - Johan Larsson, Jonas Wallin:
The Choice of Normalization Influences Shrinkage in Regularized Regression. - Keishi Sando, Tam Le, Hideitsu Hino:
Tree Structure for the Categorical Wasserstein Weisfeiler-Lehman Graph Kernel. - Mengwei Yang, Baturalp Buyukates, Athina Markopoulou:
Rewarding the Rare: Maverick-Aware Shapley Valuation in Federated Learning. - Minhyun Lee, Seungho Lee, Song Park, Dongyoon Han, Byeongho Heo, Hyunjung Shim:
MaskRIS: Semantic Distortion-aware Data Augmentation for Referring Image Segmentation. - Hossein Babaei, Mel White, Sina Alemohammad, Richard G. Baraniuk:
SaFARi: State-Space Models for Frame-Agnostic Representation. - Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell:
Enhancing Physics-Informed Neural Networks Through Feature Engineering. - Leonidas Gee, Wing Yan Li, Viktoriia Sharmanska, Novi Quadrianto:
Visual-Word Tokenizer: Beyond Fixed Sets of Tokens in Vision Transformers. - Shankhanil Mitra, Diptanu De, Shika Rao, Rajiv Soundararajan:
Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization. - Winfried van den Dool, Maksim Zhdanov, Yuki M. Asano, Max Welling:
Adaptive Mesh Quantization for Neural PDE Solvers. - Anders Vestergaard Nørskov, Kasper Jørgensen, Alexander Neergaard Zahid, Morten Mørup:
Estimating the Event-Related Potential from Few EEG Trials. - Amir Saeidi, Shivanshu Verma, Kashif Rasul, Aswin RRV, Chitta Baral:
Triple Preference Optimization: Achieving Better Alignment using a Single Step Optimization. - Anirudh Satheesh, Anant Khandelwal, Mucong Ding, Radu Balan:
PICore: Physics-Informed Unsupervised Coreset Selection for Data Efficient Neural Operator Training. - Dana Weitzner, Mauricio Delbracio, Peyman Milanfar, Raja Giryes:
The Diffusion Process as a Correlation Machine: Linear Denoising Insights. - Adrien Benamira, Tristan Guérand, Thomas Peyrin, Sayandeep Saha:
TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture. - David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark:
Harmonic Loss Trains Interpretable AI Models. - Jiefeng Chen, Jie Ren, Xinyun Chen, Chengrun Yang, Ruoxi Sun, Jinsung Yoon, Sercan Ö. Arik:
SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling. - Arnesh Batra, Dev Sharma, Krish Thukral, Ruhani Bhatia, Naman Batra, Aditya Gautam:
Melody or Machine: Detecting Synthetic Music with Dual-Stream Contrastive Learning. - Giorgos Sfikas, George Retsinas:
Unlocking the matrix form of the Quaternion Fourier Transform and Quaternion Convolution: Properties, connections, and application to Lipschitz constant bounding. - Hsin-Ying Lee, Kelvin C. K. Chan, Ming-Hsuan Yang:
CoCoIns: Consistent Subject Generation via Contrastive Instantiated Concepts. - Melissa Adrian, Jake A. Soloff, Rebecca Willett:
Stabilizing black-box model selection with the inflated argmax. - Bin Luo, Susan Halabi:
Sparse-Input Neural Network using Group Concave Regularization. - Jinkun Cao, Jingyuan Liu, Kris Kitani, Yi Zhou:
Joint Diffusion for Universal Hand-Object Grasp Generation. - Benjamin Turtel, Danny Franklin, Kris Skotheim, Luke Hewitt, Philipp Schoenegger:
Outcome-based Reinforcement Learning to Predict the Future. - Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mido Assran:
A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs. - Dimitris Bertsimas, Caio de Prospero Iglesias, Nicholas A. G. Johnson:
Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations. - Andy Li, Aiden Durrant, Milan Markovic, Tianjin Huang, Souvik Kundu, Tianlong Chen, Lu Yin, Georgios Leontidis:
Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning. - Fouad Alkhoury, Tamás Horváth, Christian Bauckhage, Stefan Wrobel:
Learning to Rank Features to Enhance Graph Neural Networks for Graph Classification. - Parastoo Pashmchi, Jerome Benoit, Motonobu Kanagawa:
kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions. - Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal:
Decentralized Projection-free Online Upper-Linearizable Optimization with Applications to DR-Submodular Optimization. - Jonathan Wenshøj, Bob Pepin, Raghavendra Selvan:
Oscillations Make Neural Networks Robust to Quantization. - Cen-Jhih Li, Aditya Bhaskara:
An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning. - Yijin Zeng, Niall M. Adams, Dean A. Bodenham:
MMD Two-sample Testing in the Presence of Arbitrarily Missing Data. - Guangyi Liu, Pengxiang Zhao, Yaozhen Liang, Liang Liu, Yaxuan Guo, Han Xiao, Weifeng Lin, Yuxiang Chai, Yue Han, Shuai Ren, Hao Wang, Xiaoyu Liang, WenHao Wang, Tianze Wu, Zhengxi Lu, Siheng Chen, LiLinghao, Hao Wang, Guanjing Xiong, Yong Liu, Hongsheng Li:
LLM-Powered GUI Agents in Phone Automation: Surveying Progress and Prospects. - Hamza Fawzi, Omar Fawzi:
Convergence of linear programming hierarchies for Gibbs states of spin systems. - Zelin Zang, Chenrui Duan, Siyuan Li, Jinlin Wu, BingoWing-Kuen Ling, Fuji Yang, Jiebo Luo, Zhen Lei, Stan Z. Li:
MDTree: A Masked Dynamic Autoregressive Model for Phylogenetic Inference. - Xintong Duan, Yutong He, Fahim Tajwar, Wentse Chen, Ruslan Salakhutdinov, Jeff G. Schneider:
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models. - Louk van Remmerden, Zhao Yang, Shujian Yu, Mark Hoogendoorn, Vincent François-Lavet:
Learning Task-Aware Abstract Representations for Meta-Reinforcement Learning. - Jérôme Bolte, Ryan Boustany, Edouard Pauwels, Andrei I. Purica:
A second-order-like optimizer with adaptive gradient scaling for deep learning. - Cameron Omid Smith, Basile Van Hoorick, Chonghyuk Song, Vincent Sitzmann, Vitor Campagnolo Guizilini, Yue Wang:
SIRE: SE(3) Intrinsic Rigidity Embeddings. - Zixia Jia, Jiaqi Li, Yipeng Kang, Yuxuan Wang, Tong Wu, Quansen Wang, Xiaobo Wang, Shuyi Zhang, Junzhe Shen, Qing Li, Siyuan Qi, Yitao Liang, Di He, Zilong Zheng, Song-Chun Zhu:
The AI Hippocampus: How Far are We From Human Memory? - Parham Rezaei, Arash Mari Oriyad, Mahdieh Soleymani Baghshah, Mohammad Hossein Rohban:
Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models. - Madison Cooley, Mike Kirby, Shandian Zhe, Varun Shankar:
HyResPINNs: A Hybrid Residual Physics-Informed Neural Network Architecture Designed to Balance Expressiveness and Trainability. - Lorenz Wolf, Mirco Musolesi:
Heterogeneous Knowledge for Augmented Modular Reinforcement Learning. - Shengzhong Zhang, Wenjie Yang, Yimin Zhang, Hongwei Zhang, Zengfeng Huang:
Understanding Class Bias Amplification in Graph Representation Learning. - Rares Dolga, Lucas Maystre, Marius Cobzarenco, David Barber:
Unifying Linear-Time Attention via Latent Probabilistic Modelling. - Barys Liskavets, Shuvendu Roy, Maxim Ushakov, Mark Klibanov, Ali Etemad, Shane K. Luke:
Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor. - Vasileios Sevetlidis, George Pavlidis, Antonios Gasteratos:
Angular Regularization for Positive-Unlabeled Learning on the Hypersphere. - Elliott Thornley, Alexander Roman, Christos Ziakas, Louis Thomson, Leyton Ho:
Towards shutdownable agents via stochastic choice. - Shifeng Xie, Rui Yuan, Simone Rossi, Thomas Hannagan:
The Initialization Determines Whether In-Context Learning Is Gradient Descent. - Pedro P. Sanchez, Damian Machlanski, Steven McDonagh, Sotirios A. Tsaftaris:
Causal Ordering for Structure Learning from Time Series. - Yuchen Tian, Samuel Tensingh, Jason Eshraghian, Nhan Duy Truong, Omid Kavehei:
Synchrony-Gated Plasticity with Dopamine Modulation for Spiking Neural Networks. - Wassim Bouaziz, Nicolas Usunier, El-Mahdi El-Mhamdi:
Inverting Gradient Attacks Makes Powerful Data Poisoning. - Tim Johnston, Iosif Lytras, Nikolaos Makras, Sotirios Sabanis:
The Performance Of The Unadjusted Langevin Algorithm Without Smoothness Assumptions. - Duc-Duy Nguyen, Dat Nguyen:
VirDA: Reusing Backbone for Unsupervised Domain Adaptation with Visual Reprogramming. - Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha, Samrat Mondal:
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning. - Hongliang Ni, Tong Chen, Shazia Sadiq, Gianluca Demartini:
Denoising Pretrained Black-box Models via Amplitude-Guided Phase Realignment. - Noah Flynn:
COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling. - Ayan Sengupta, Yash Goel, Tanmoy Chakraborty:
How to Upscale Neural Networks with Scaling Law? - Yifan Wang, Sukrut Rao, Ji-Ung Lee, Mayank Jobanputra, Vera Demberg:
B-cos LM: Efficiently Transforming Pre-trained Language Models for Improved Explainability. - Zachary Coalson, Huazheng Wang, Qingyun Wu, Sanghyun Hong:
Hard Work Does Not Always Pay Off: On the Robustness of NAS to Data Poisoning. - Jens-Michalis Papaioannou, Alexei Figueroa, Conor Fallon, Anna Capilla, Alexandra Bekiaridou, Stavros Zanos, Wolfgang Nejdl, Alexander Löser:
SPONGE: Competing Sparse Language Representations for Effective Knowledge Transfer. - Laurène Vaugrante, Mathias Niepert, Thilo Hagendorff:
Prompt Engineering Techniques for Language Model Reasoning Lack Replicability. - Benoit Dherin, Benny Avelin, Anders Karlsson, Hanna Mazzawi, Javier Gonzalvo, Michael Munn:
How iteration composition influences convergence and stability in deep learning. - George R. Nahass, Zhu Wang, Homa Rashidisabet, Won Hwa Kim, Sasha Hubschman, Jeffrey C. Peterson, Pete Setabutr, Chad A. Purnell, Ann Q. Tran, Darvin Yi, Sathya N. Ravi:
Targeted Unlearning Using Perturbed Sign Gradient Methods With Applications On Medical Images. - Moritz Weckbecker, Galip Ümit Yolcu, Thomas Wiegand, Wojciech Samek, Sebastian Lapuschkin:
Sparse, Efficient and Explainable Data Attribution with DualXDA. - Justus Westerhoff, Golzar Atefi, Mario Koddenbrock, Alexei Figueroa, Alexander Löser, Erik Rodner, Felix Alexander Gers:
Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation. - Pranav Ramesh, Gopalakrishnan Srinivasan:
PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation. - Alexia Jolicoeur-Martineau, Aristide Baratin, Kisoo Kwon, Boris Knyazev, Yan Zhang:
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees. - Minkyu Choi, Yunhao Yang, Neel P. Bhatt, Kushagra Gupta, Sahil Shah, Aditya Rai, David Fridovich-Keil, Ufuk Topcu, Sandeep P. Chinchali:
Real-Time Privacy Preservation for Robot Visual Perception. - M. M. Amaan Valiuddin, Ruud van Sloun, Christiaan G. A. Viviers, Peter H. N. de With, Fons van der Sommen:
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation. - Prateek Chhikara:
Mind the Confidence Gap: Overconfidence, Calibration, and Distractor Effects in Large Language Models. - Max W. Shen, Ewa M. Nowara, Michael Maser, Kyunghyun Cho:
Training Dynamics of Learning 3D-Rotational Equivariance. - Zihan Chen, Xingbo Fu, Yushun Dong, Jundong Li, Cong Shen:
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs. - Dan Kalifa, Uriel Singer, Kira Radinsky:
FusionProt: Fusing Sequence and Structural Information for Unified Protein Representation Learning. - Yuan Yin, Shashanka Venkataramanan, Tuan-Hung Vu, Andrei Bursuc, Matthieu Cord:
IPA: An Information-Reconstructive Input Projection Framework for Efficient Foundation Model Adaptation. - Netanel Fried, Liad Giladi, Gilad Katz:
Generative Proto-Sequence: Sequence-Level Decision Making for Long-Horizon Reinforcement Learning. - Leonard Bereska, Zoe Tzifa-Kratira, Reza Samavi, Stratis Gavves:
Superposition as Lossy Compression - Measure with Sparse Autoencoders and Connect to Adversarial Vulnerability. - Sinho Chewi, Philippe Rigollet, Yuling Yan:
Gaussian mixture layers for neural networks. - Jonathan Hyun, Nicholas R. Waytowich, Boyuan Chen:
CREW-Wildfire: Benchmarking Agentic Multi-Agent Collaborations at Scale. - Mahsa Taheri, Fang Xie, Johannes Lederer:
Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks. - Nils Neukirch, Johanna Vielhaben, Nils Strodthoff:
FeatInv: Spatially resolved mapping from feature space to input space using conditional diffusion models. - Siddhant Arora, Kai-Wei Chang, Chung-Ming Chien, Yifan Peng, Haibin Wu, Yossi Adi, Emmanuel Dupoux, Hung-yi Lee, Karen Livescu, Shinji Watanabe:
On The Landscape of Spoken Language Models: A Comprehensive Survey. - Fahad Sarfraz, Bahram Zonooz, Elahe Arani:
Consistency Aware Robust Learning under Noisy Labels. - Gautam Sreekumar, Vishnu Boddeti:
Incorporating Interventional Independence Improves Robustness against Interventional Distribution Shift. - Eshed Gal, Moshe Eliasof, Carola-Bibiane Schönlieb, Ivan I. Kyrchei, Eldad Haber, Eran Treister:
Towards Efficient Training of Graph Neural Networks: A Multiscale Approach. - Noam Issachar, Mohammad Salama, Raanan Fattal, Sagie Benaim:
Designing a Conditional Prior Distribution for Flow-Based Generative Models. - Zihao Li, Dongqi Fu, Hengyu Liu, Jingrui He:
Hypergraphs as Weighted Directed Self-Looped Graphs: Spectral Properties, Clustering, Cheeger Inequality. - Benjamin Rodatz, Ian Fan, Tuomas Laakkonen, Neil John Ortega, Thomas Hoffmann, Vincent Wang:
A Pattern Language for Machine Learning Tasks. - Ian Colbert, Giuseppe Franco, Fabian Grob, Jinjie Zhang, Rayan Saab:
Accumulator-Aware Post-Training Quantization for Large Language Models. - Yasuyuki Okoshi, Hikari Otsuka, Daichi Fujiki, Masato Motomura:
TicketLLM: Next-Generation Sparse and Low-bit Transformers with Supermask-based Method. - Aryo Pradipta Gema, Alexander Hägele, Runjin Chen, Andy Arditi, Jacob Goldman-Wetzler, Kit Fraser-Taliente, Henry Sleight, Linda Petrini, Julian Michael, Beatrice Alex, Pasquale Minervini, Yanda Chen, Joe Benton, Ethan Perez:
Inverse Scaling in Test-Time Compute. - Thomas De Min, Subhankar Roy, Stéphane Lathuilière, Elisa Ricci, Massimiliano Mancini:
Group-robust Machine Unlearning. - Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting:
Tractable Representation Learning with Probabilistic Circuits. - David Mark Bossens, Atsushi Nitanda:
Mirror Descent Policy Optimisation for Robust Constrained Markov Decision Processes. - Siemen Herremans, Ali Anwar, Siegfried Mercelis:
Robust Reinforcement Learning in a Sample-Efficient Setting. - Mominul Rubel, Adam Meyers, Gabriel Nicolosi:
Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning. - Reena Elangovan, Charbel Sakr, Anand Raghunathan, Brucek Khailany:
LO-BCQ: Locally Optimal Block Clustered Quantization for 4-bit (W4A4) LLM Inference. - Marimuthu Kalimuthu, David Holzmüller, Mathias Niepert:
LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators. - Xingyu Su, Xiner Li, Yuchao Lin, Ziqian Xie, Degui Zhi, Shuiwang Ji:
Language Models for Controllable DNA Sequence Design. - Augusto Tagle, Javier Ruiz-del-Solar, Felipe Tobar:
Diffusion Self-Weighted Guidance for Offline Reinforcement Learning. - Namjoon Suh, Yuning Yang, Din-Yin Hsieh, Qitong Luan, Shirong Xu, Shixiang Zhu, Guang Cheng:
TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data. - Francesco Montagna, Max Cairney-Leeming, Dhanya Sridhar, Francesco Locatello:
Demystifying amortized causal discovery with transformers. - Shi Quan Foo, Chi-Ho Wong, Zhihan Gao, Dit-Yan Yeung, Ka-Hing Wong, Wai-Kin Wong:
STLDM: Spatio-Temporal Latent Diffusion Model for Precipitation Nowcasting. - Haodong Lu, Xinyu Zhang, Kristen Moore, Minhui Xue, Lina Yao, Anton van den Hengel, Dong Gong:
Continual Learning on CLIP via Incremental Prompt Tuning with Intrinsic Textual Anchors. - Dong-Sig Han, Jaein Kim, Hee Bin Yoo, Byoung-Tak Zhang:
Variational Online Mirror Descent for Robust Learning in Schrödinger Bridge. - André Artelt, Martin Olsen, Kevin Tierney:
On the Hardness of Computing Counterfactual and Semi-factual Explanations in XAI. - Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L. Dyer:
GraphFM: A generalist graph transformer that learns transferable representations across diverse domains. - An Vuong, Michael Thompson McCann, Javier E. Santos, Yen Ting Lin:
Are We Really Learning the Score Function? Reinterpreting Diffusion Models Through Wasserstein Gradient Flow Matching. - Christopher Yeh, Nicolas Christianson, Alan Wu, Adam Wierman, Yisong Yue:
End-to-End Conformal Calibration for Optimization Under Uncertainty. - Zubair Bashir, Bhavik Chandna, Procheta Sen:
Dissecting Bias in LLMs: A Mechanistic Interpretability Perspective. - Eric Chen, Tiffany Tang, Genevera I. Allen:
Top-$k$ Feature Importance Ranking. - Zeyuan Allen-Zhu, Yuanzhi Li:
Physics of Language Models: Part 1, Learning Hierarchical Language Structures. - Xu Ouyang, Shengzhuang Chen, Michael Arthur Leopold Pearce, Thomas Hartvigsen, Jonathan Richard Schwarz:
ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimization. - Weimin Bai, Yubo Li, Wenzheng Chen, Weijian Luo, He Sun:
Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching. - Tianyi Xiang, Yangyang Xu, Qingxuan Hu, Chenyi Zi, Nanxuan Zhao, Junle Wang, Shengfeng He:
Let Your Light Shine: Foreground Portrait Matting via Deep Flash Priors. - Felix Bok, Atanas Mirchev, Baris Kayalibay, Ole Jonas Wenzel, Patrick van der Smagt, Justin Bayer:
Inherently Robust Control through Maximum-Entropy Learning-Based Rollout. - Haoyi Qiu, Kung-Hsiang Huang, Ruichen Zheng, Jiao Sun, Nanyun Peng:
Multimodal Cultural Safety: Evaluation Framework and Alignment Strategies. - Zhanyu Wang, Chen Tang, Haoyu He, Kuan Feng, Chao Wang, Bingni Zhang, Xiaolei XU, Shen Wang, Luping Zhou:
TempFlex: Advancing MLLMs with Temporal Perception and Natively Scalable Resolution Encoding. - Shivam Pal, Aishwarya Gupta, Saqib Sarwar, Piyush Rai:
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization. - Ann-Kathrin Kiessner, Tonio Ball, Joschka Boedecker:
Multi-BK-Net: Multi-Branch Multi-Kernel Convolutional Neural Networks for Clinical EEG Analysis. - Milad Abdollahzadeh, Guimeng Liu, Touba Malekzadeh, Christopher T. H. Teo, Keshigeyan Chandrasegaran, Ngai-Man Cheung:
A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot. - Noel Teku, Fengwei Tian, Payel Bhattacharjee, Souradip Chakraborty, Amrit Singh Bedi, Ravi Tandon:
PROPS: Progressively Private Self-alignment of Large Language Models. - Robert Joseph George, Suozhi Huang, Peiyang Song, Anima Anandkumar:
LeanProgress: Guiding Search for Neural Theorem Proving via Proof Progress Prediction. - Mohamad H. Danesh, Khanh Xuan Nguyen, Tu Trinh, Benjamin Plaut:
YRC-Bench: A Benchmark for Learning to Coordinate with Experts. - Yicheng He, Zhou Kaiyu, Haoyue Bai, Fengbin Zhu, Yonghui Yang:
Understanding Embedding Scaling in Collaborative Filtering. - Maxime Poli, Mahi Luthra, Youssef Benchekroun, Yosuke Higuchi, Martin Gleize, Jiayi Shen, Robin Algayres, Yu-An Chung, Mido Assran, Juan Pino, Emmanuel Dupoux:
SpidR: Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervision. - Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan:
Are Data Embeddings Effective in Time Series Forecasting? - Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon:
Amortized Inference of Causal Models via Conditional Fixed-Point Iterations. - Xingjian Zhou, Keyi Shen, Andy Xu, Hongji Xu, Cho-Jui Hsieh, Huan Zhang, Zhouxing Shi:
SoundnessBench: A Soundness Benchmark for Neural Network Verifiers. - Jiaxu Qian, Chendong Wang, Yifan Yang, Chaoyun Zhang, Huiqiang Jiang, Xufang Luo, Yu Kang, Qingwei Lin, Anlan Zhang, Shiqi Jiang, Ting Cao, Tianjun Mao, Suman Banerjee, Guyue Liu, Saravan Rajmohan, Dongmei Zhang, Yuqing Yang, Qi Zhang, Lili Qiu:
Zoomer: Adaptive Image Focus Optimization for Black-box MLLM. - Xiangsen Chen, Xuan Feng, Shuo Chen, Matthieu Maitre, Sudipto Rakshit, Diana Duvieilh, Ashley Picone, Nan Tang:
CyberThreat-Eval: Can Large Language Models Automate Real-World Threat Research? - Giorgio Franceschelli, Mirco Musolesi:
DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation. - Yaqian Zhang, Bernhard Pfahringer, Eibe Frank, Albert Bifet:
Rethinking Memory in Continual Learning: Beyond a Monolithic Store of the Past. - Yuxiang Huang, Binhang Yuan, Xu Han, Chaojun Xiao, Zhiyuan Liu:
Locret: Enhancing Eviction in Long-Context LLM Inference with Trained Retaining Heads on Consumer-Grade Devices. - Tianhong Li, Qinyi Sun, Lijie Fan, Kaiming He:
Fractal Generative Models. - Muhammad Bilal Shahid, Cody H. Fleming:
HopCast: Calibration of Autoregressive Dynamics Models. - Longtian Qiu, Shan Ning, Chuyu Zhang, Jiaxuan Sun, Xuming He:
DA-DPO: Cost-efficient Difficulty-aware Preference Optimization for Reducing MLLM Hallucinations. - Daniel P. Sawyer, Nan Rosemary Ke, Hubert Soyer, Martin Engelcke, John Reid, David P. Reichert, Drew A. Hudson, Alexander Lerchner, Danilo Jimenez Rezende, Timothy P. Lillicrap, Michael Curtis Mozer, Jane X. Wang:
Exploring exploration with foundation agents in interactive environments. - Hojun Son, Asma Almutairi, Arpan Kusari:
Quantifying Context Bias in Domain Adaptation for Object Detection. - Eric Gan, Patrik Reizinger, Alice Bizeul, Attila Juhos, Mark Ibrahim, Randall Balestriero, David A. Klindt, Wieland Brendel, Baharan Mirzasoleiman:
Occam's Razor for SSL: Memory-Efficient Parametric Instance Discrimination. - Prasanna Devadiga, Kishan Gurumurthy, Kshitij Mohan:
Universal Differential Equations for Stable Multi-Step Volatility Time Series Forecasting. - Sumedh Pendurkar, Guni Sharon:
Policy-Guided Search on Tree-of-Thoughts for Efficient Problem Solving with Bounded Language Model Queries. - William E. R. de Amorim, Scott A. Sisson, Thais Carvalho Valadares Rodrigues, David J. Nott, Guilherme S. Rodrigues:
Positional Encoder Graph Quantile Neural Networks for Geographic Data. - Yao Lu, Zhaiyuan Ji, Jiawei Du, Shanqing Yu, Qi Xuan, Joey Tianyi Zhou:
AutoAnnotator: A Collaborative Annotation Framework for Large and Small Language Models. - Anson MacDonald, Scott A. Sisson, Sahani Pathiraja:
Convergence Aspects of Hybrid Kernel SVGD. - Sofien Dhouib, Steven Bilaj, Behzad Nourani-Koliji, Setareh Maghsudi:
Cluster Agnostic Network Lasso Bandits. - Sahil Verma, Royi Rassin, Arnav Mohanty Das, Gantavya Bhatt, Preethi Seshadri, Chirag Shah, Jeff A. Bilmes, Hannaneh Hajishirzi, Yanai Elazar:
How Many Images Does It Take? Estimating Imitation Thresholds in Text-to-Image Models. - Dagmara Panas, Ali Payani, Vaishak Belle:
Unreasonable effectiveness of LLM reasoning: a doubly cautionary tale of temporal question-answering. - Md. Tanvir Alam, Md. Ahasanul Alam, Md Mahmudur Rahman, Md. Mosaddek Khan:
Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases. - Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino, Richard E. Turner:
Efficient Few-Shot Continual Learning in Vision-Language Models.
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