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ORCID38th COLT 2025: Lyon, France
- Nika Haghtalab, Ankur Moitra:
The Thirty Eighth Annual Conference on Learning Theory, June 30 - July 4, 2025, Lyon, France. Proceedings of Machine Learning Research 291, PMLR 2025 - Nika Haghtalab, Ankur Moitra:
Conference on Learning Theory 2025: Preface. i - Gianmarco Genalti, Alberto Maria Metelli:
Open Problem: Regret Minimization in Heavy-Tailed Bandits with Unknown Distributional Parameters. 1-5 - Priyank Agrawal, Shipra Agrawal:
Optimistic Q-learning for average reward and episodic reinforcement learning extended abstract. 1 - Syed Akbari, Matthew Harrison-Trainor:
Computable learning of natural hypothesis classes. 2-21 - Maryam Aliakbarpour, Arnav Burudgunte, Clément L. Canonne, Ronitt Rubinfeld:
Better Private Distribution Testing by Leveraging Unverified Auxiliary Data. 22-63 - Alexander Appel, Vanessa Kosoy:
Regret Bounds for Robust Online Decision Making. 64-146 - Hassan Ashtiani, Vinayak Pathak, Ruth Urner:
Simplifying Adversarially Robust PAC Learning With Tolerance. 147-168 - Angelos Assos, Yuval Dagan, Nived Rajaraman:
Computational Intractability of Strategizing against Online Learners. 169-199 - Samuel Baguley, Andreas Göbel, Marcus Pappik, Leon Schiller:
Testing Thresholds and Spectral Properties of High-Dimensional Random Toroidal Graphs via Edgeworth-Style Expansions. 200-201 - Cedar Site Bai, Brian Bullins:
Faster Acceleration for Steepest Descent. 202-230 - Alireza Bakhtiari, Tor Lattimore, Csaba Szepesvári:
Thompson Sampling for Bandit Convex Optimisation. 231-263 - Ainesh Bakshi, Vincent Cohen-Addad, Rajesh Jayaram, Sam Hopkins, Silvio Lattanzi:
Metric Embeddings Beyond Bi-Lipschitz Distortion via Sherali-Adams. 264-279 - Robi Bhattacharjee, Karolin Frohnapfel, Ulrike von Luxburg:
How to safely discard features based on aggregate SHAP values. 280-314 - Hadley Black, Arya Mazumdar, Barna Saha, Yinzhan Xu:
Optimal Graph Reconstruction by Counting Connected Components in Induced Subgraphs. 315-343 - Hadley Black, Arya Mazumdar, Barna Saha:
Learning Partitions with Optimal Query and Round Complexities. 344-374 - Guy Blanc, Jane Lange, Carmen Strassle, Li-Yang Tan:
A Distributional-Lifting Theorem for PAC Learning. 375-379 - Ari Blondal, Shan Gao, Hamed Hatami, Pooya Hatami:
Stability and List-Replicability for Agnostic Learners. 380-400 - Avrim Blum, Steve Hanneke, Chirag Pabbaraju, Donya Saless:
Proofs as Explanations: Short Certificates for Reliable Predictions. 401-420 - Jinho Bok, Jason M. Altschuler:
Accelerating Proximal Gradient Descent via Silver Stepsizes. 421-453 - Victor Boone, Bruno Gaujal:
Logarithmic regret of exploration in average reward Markov decision processes. 454-533 - Guy Bresler, Chenghao Guo, Yury Polyanskiy, Andrew Yao:
Partial and Exact Recovery of a Random Hypergraph from its Graph Projection. 534-593 - Guy Bresler, Alina Harbuzova:
Computational Equivalence of Spiked Covariance and Spiked Wigner Models via Gram-Schmidt Perturbation. 594-595 - Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen:
Of Dice and Games: A Theory of Generalized Boosting. 596-640 - Marco Bressan, Nataly Brukhim, Nicolò Cesa-Bianchi, Emmanuel Esposito, Yishay Mansour, Shay Moran, Maximilian Thiessen:
A Fine-grained Characterization of PAC Learnability. 641-676 - Yang Cai, Siddharth Mitra, Xiuyuan Wang, Andre Wibisono:
On the Convergence of Min-Max Langevin Dynamics and Algorithm. 677-754 - Yang Cai, Alkis Kalavasis, Katerina Mamali, Anay Mehrotra, Manolis Zampetakis:
What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness (Extended Abstract). 755-756 - Francesco Camilli, Daria Tieplova, Eleonora Bergamin, Jean Barbier:
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime. 757-798 - Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Luigi Foscari, Vinayak Pathak:
Market Making without Regret. 799-837 - Diptarka Chakraborty, Kushagra Chatterjee, Debarati Das, Tien Long Nguyen, Romina Nobahari:
Towards Fair Representation: Clustering and Consensus. 838-853 - Moses Charikar, Chirag Pabbaraju:
Exploring Facets of Language Generation in the Limit. 854-887 - Zachary Chase, Idan Mehalel:
Deterministic Apple Tasting. 888-923 - Jerry Chee, Arturs Backurs, Rainie Heck, Li Zhang, Janardhan Kulkarni, Thomas Rothvoss, Sivakanth Gopi:
DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory. 924-951 - Lesi Chen, Chengchang Liu, Luo Luo, Jingzhao Zhang:
Solving Convex-Concave Problems with 풪(ε-4/7) Second-Order Oracle Complexity. 952-982 - Fan Chen, Alexander Rakhlin:
Decision Making in Changing Environments: Robustness, Query-Based Learning, and Differential Privacy. 983-985 - Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li:
Predicting quantum channels over general product distributions. 986-1007 - Kean Chen, Qisheng Wang:
Improved sample upper and lower bounds for trace estimation of quantum state powers. 1008-1028 - Sitan Chen, Vasilis Kontonis, Kulin Shah:
Learning general Gaussian mixtures with efficient score matching. 1029-1090 - Xue Chen, Wenxuan Shu, Zhaienhe Zhou:
Algorithms for Sparse LPN and LSPN Against Low-noise (extended abstract). 1091-1093 - August Y. Chen, Karthik Sridharan:
Optimization, Isoperimetric Inequalities, and Sampling via Lyapunov Potentials. 1094-1153 - Yeshwanth Cherapanamjeri, Daniel Lee:
Heavy-tailed Estimation is Easier than Adversarial Contamination. 1154-1184 - Yeshwanth Cherapanamjeri, Sumegba Garg, Nived Rajaraman, Ayush Sekhari, Abhishek Shetty:
The Space Complexity of Learning-Unlearning Algorithms (extended abstract). 1185-1193 - Nai-Hui Chia, Daniel Liang, Fang Song:
Quantum State and Unitary Learning Implies Circuit Lower Bounds. 1194-1252 - Byron Chin, Elchanan Mossel, Youngtak Sohn, Alexander S. Wein:
Stochastic block models with many communities and the Kesten-Stigum bound - extended abstract. 1253-1258 - Bogdan Chornomaz, Shay Moran, Tom Waknine:
Spherical Dimension. 1259-1313 - Spencer Compton, Chirag Pabbaraju, Nikita Zhivotovskiy:
Lower Bounds for Greedy Teaching Set Constructions. 1314-1329 - Juan Pablo Contreras, Cristóbal Guzmán, David Martínez-Rubio:
Non-Euclidean High-Order Smooth Convex Optimization Extended Abstract. 1330 - Elisabetta Cornacchia, Dan Mikulincer, Elchanan Mossel:
Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions. 1331-1365 - Yan Dai, Moïse Blanchard, Patrick Jaillet:
Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely (Extended Abstract). 1366-1367 - Amit Daniely:
Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on $\mathbb{SO}(d)$. 1368-1379 - Christoph Dann, Yishay Mansour, Mehryar Mohri, Jon Schneider, Balasubramanian Sivan:
Rate-Preserving Reductions for Blackwell Approachability. 1380-1414 - Arif Kerem Dayi, Sitan Chen:
Low-rank fine-tuning lies between lazy training and feature learning. 1415-1471 - Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos:
Learning Intersections of Two Margin Halfspaces under Factorizable Distributions. 1472-1530 - Ilias Diakonikolas, Daniel M. Kane, Lisheng Ren:
Faster Algorithms for Agnostically Learning Disjunctions and their Implications. 1531-1558 - Hang Du, Shuyang Gong, Jiaming Xu:
A Proof of The Changepoint Detection Threshold Conjecture in Preferential Attachment Models. 1559-1563 - Cynthia Dwork, Chris Hays, Nicole Immorlica, Juan C. Perdomo, Pranay Tankala:
From Fairness to Infinity: Outcome-Indistinguishable (Omni)Prediction in Evolving Graphs. 1564-1637 - Amitsour Egosi, Gilad Yehudai, Ohad Shamir:
Logarithmic Width Suffices for Robust Memorization. 1638-1690 - Dor Elimelech, Wasim Huleihel:
Detecting Arbitrary Planted Subgraphs in Random Graphs. 1691-1798 - Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern:
Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation. 1799-1918 - Chenglin Fan, Kijun Shin:
Learning Augmented Graph k-Clustering. 1919-1934 - Vitaly Feldman, Guy Kornowski, Xin Lyu:
Trade-offs in Data Memorization via Strong Data Processing Inequalities. 1935-1973 - Weiming Feng, Hongyang Liu, Minji Yang:
Approximating the total variation distance between spin systems. 1974-2025 - Dylan J. Foster, Zakaria Mhammedi, Dhruv Rohatgi:
Is a Good Foundation Necessary for Efficient Reinforcement Learning? The Computational Role of the Base Model in Exploration. 2026-2142 - Alexandre François, Antonio Orvieto, Francis R. Bach:
An uncertainty principle for Linear Recurrent Neural Networks. 2143-2187 - Vincent Froese, Moritz Grillo, Martin Skutella:
Complexity of Injectivity and Verification of ReLU Neural Networks (Extended Abstract). 2188-2189 - Kaito Fujii:
Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy. 2190-2191 - Wenzhi Gao, Ya-Chi Chu, Yinyu Ye, Madeleine Udell:
Gradient Methods with Online Scaling. 2192-2226 - Chao Gao, Liren Shan, Vaidehi Srinivas, Aravindan Vijayaraghavan:
Computing High-dimensional Confidence Sets for Arbitrary Distributions. 2227-2269 - Dan Garber, Mhna Massalha:
Blackwell's Approachability with Approximation Algorithms. 2270-2290 - Sachin Garg, Michal Derezinski:
Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method. 2291-2325 - Nicolas Gast, Dheeraj Narasimha:
Model predictive control is almost optimal for restless bandits. 2326-2361 - Khashayar Gatmiry, Jon Schneider, Stefanie Jegelka:
Computing Optimal Regularizers for Online Linear Optimization. 2362-2402 - Khashayar Gatmiry, Jonathan A. Kelner, Holden Lee:
Learning Mixtures of Gaussians Using Diffusion Models. 2403-2456 - Julia Gaudio, Colin Sandon, Jiaming Xu, Dana Yang:
"All-Something-Nothing" Phase Transitions in Planted k-Factor Recovery (Extended Abstract). 2457-2459 - Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Sushant Sachdeva:
PREM: Privately Answering Statistical Queries with Relative Error. 2460 - Margalit Glasgow, Denny Wu, Joan Bruna:
Mean-field analysis of polynomial-width two-layer neural network beyond finite time horizon. 2461-2539 - Yuzhou Gu, Xin Li, Yinzhan Xu:
Tight Bounds for Noisy Computation of High-Influence Functions, Connectivity, and Threshold. 2540-2591 - Anxin Guo, Aravindan Vijayaraghavan:
Agnostic Learning of Arbitrary ReLU Activation under Gaussian Marginals. 2592-2631 - Soumita Hait, Ping Li, Haipeng Luo, Mengxiao Zhang:
Alternating Regret for Online Convex Optimization. 2632-2633 - Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff:
Data Selection for ERMs. 2634-2665 - Steve Hanneke, Mingyue Xu:
Universal Rates of ERM for Agnostic Learning. 2666-2703 - Steve Hanneke, Amirreza Shaeiri, Qian Zhang:
Universal Rates for Multiclass Learning with Bandit Feedback. 2704-2756 - Themistoklis Haris, Krzysztof Onak:
Compression Barriers in Autoregressive Transformers. 2757-2785 - Yuchen He, Chihao Zhang:
On the query complexity of sampling from non-log-concave distributions (extended abstract). 2786-2787 - Mohsen Heidari, Roni Khardon:
Learning DNF through Generalized Fourier Representations. 2788-2804 - Lukas Hintze, Lena Krieg, Olga Scheftelowitsch, Haodong Zhu:
Noisy Group Testing in the Linear Regime: Exact Thresholds and Efficient. 2805-2821 - Mikael Møller Høgsgaard, Kasper Green Larsen:
Improved Margin Generalization Bounds for Voting Classifiers. 2822-2855 - Han Huang, Elchanan Mossel:
Polynomial low degree hardness for Broadcasting on Trees (Extended Abstract). 2856-2857 - Shinji Ito, Haipeng Luo, Taira Tsuchiya, Yue Wu:
Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback. 2858-2892 - Valentio Iverson, Gautam Kamath, Argyris Mouzakis:
Optimal Differentially Private Sampling of Unbounded Gaussians. 2893-2941 - Sky Jafar, Julian Asilis, Shaddin Dughmi:
Local Regularizers Are Not Transductive Learners. 2942-2957 - Zeyu Jia, Alexander Rakhlin, Yury Polyanskiy:
On the Minimax Regret of Sequential Probability Assignment via Square-Root Entropy. 2958-3016 - Minhui Jiang, Yuansi Chen:
Regularized Dikin Walks for Sampling Truncated Logconcave Measures, Mixed Isoperimetry and Beyond Worst-Case Analysis. 3017-3078 - Liwei Jiang, Abhishek Roy, Krishna Balasubramanian, Damek Davis, Dmitriy Drusvyatskiy, Sen Na:
Online Covariance Estimation in Nonsmooth Stochastic Approximation. 3079-3123 - Ruichen Jiang, Devyani Maladkar, Aryan Mokhtari:
Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization. 3124-3158 - Jikai Jin, Vasilis Syrgkanis:
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation (Extended Abstract). 3159-3160 - Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel, Zhiyuan Li, Theodor Misiakiewicz, Nathan Srebro:
A Theory of Learning with Autoregressive Chain of Thought. 3161-3212 - Hadi Kazemi, Ankit Pensia, Varun S. Jog:
The Sample Complexity of Distributed Simple Binary Hypothesis Testing under Information Constraints. 3213-3214 - Seok-Jin Kim, Gi-Soo Kim, Min-hwan Oh:
Experimental Design for Semiparametric Bandits. 3215-3252 - Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan:
Learning Constant-Depth Circuits in Malicious Noise Models. 3253-3263 - Frederic Koehler, Holden Lee, Thuy-Duong Vuong:
Efficiently learning and sampling multimodal distributions with data-based initialization. 3264-3326 - Guy Kornowski, Ohad Shamir:
The Oracle Complexity of Simplex-based Matrix Games: Linear Separability and Nash Equilibria. 3327-3353 - Filip Kovacevic, Yihan Zhang, Marco Mondelli:
Spectral Estimators for Multi-Index Models: Precise Asymptotics and Optimal Weak Recovery. 3354-3404 - Akshay Krishnamurthy, Gene Li, Ayush Sekhari:
The Role of Environment Access in Agnostic Reinforcement Learning (Extended Abstract). 3405-3406 - Symantak Kumar, Purnamrita Sarkar, Kevin Tian, Yusong Zhu:
Spike-and-Slab Posterior Sampling in High Dimensions. 3407-3462 - Akash Kumar, Rahul Parhi, Mikhail Belkin:
A Gap Between the Gaussian RKHS and Neural Networks: An Infinite-Center Asymptotic Analysis. 3463-3485 - Dmitriy Kunisky:
Low coordinate degree algorithms II: Categorical signals and generalized stochastic block models. 3486-3526 - John Lazarsfeld, Georgios Piliouras, Ryann Sim, Andre Wibisono:
Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play. 3527-3577 - Daniel Lee, Francisco Pernice, Amit Rajaraman, Ilias Zadik:
The Fundamental Limits of Recovering Planted Subgraphs (extended abstract). 3578-3579 - Zhangsong Li:
Robust random graph matching in Gaussian models via vector approximate message passing. 3580-3581 - Shuangping Li, Tselil Schramm:
Some easy optimization problems have the overlap-gap property. 3582-3622 - Junfan Li, Shizhong Liao, Zenglin Xu, Liqiang Nie:
A Polynomial-time Algorithm for Online Sparse Linear Regression with Improved Regret Bound under Weaker Conditions. 3623-3670 - Qian Li, Shuo Wang, Jiapeng Zhang:
Multi-Pass Memory Lower Bounds for Learning Problems. 3671-3699 - Bo Li, Wei Wang, Peng Ye:
Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity. 3700-3722 - Jiadong Liang, Zhihan Huang, Yuxin Chen:
Low-dimensional adaptation of diffusion models: Convergence in total variation (extended abstract). 3723-3729 - Jiaming Liang, Siddharth Mitra, Andre Wibisono:
Characterizing Dependence of Samples along the Langevin Dynamics and Algorithms via Contraction of Φ-Mutual Information (Extended Abstract). 3730-3731 - Haolin Liu, Chen-Yu Wei, Julian Zimmert:
Decision Making in Hybrid Environments: A Model Aggregation Approach. 3732-3765 - Anand Louis, Rameesh Paul, Prasad Raghavendra:
Robust Algorithms for Recovering Planted r-Colorable Graphs. 3766-3794 - Zhou Lu, Y. Jennifer Sun, Zhiyu Zhang:
Sparsity-Based Interpolation of External, Internal and Swap Regret. 3795-3828 - Jiuyao Lu, Aaron Roth, Mirah Shi:
Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses. 3829-3878 - Yichen Lyu, Pengkun Yang:
Identifiability and Estimation in High-Dimensional Nonparametric Latent Structure Models. 3879-3880 - Arnab Maiti, Zhiyuan Fan, Kevin Jamieson, Lillian J. Ratliff, Gabriele Farina:
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries. 3881-3932 - Namiko Matsumoto, Arya Mazumdar:
Learning sparse generalized linear models with binary outcomes via iterative hard thresholding. 3933-4032 - Zakaria Mhammedi:
Online Convex Optimization with a Separation Oracle. 4033-4077 - Zakaria Mhammedi:
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions. 4078-4165 - Mehrdad Moharrami, Cristopher Moore, Jiaming Xu:
The Planted Spanning Tree Problems: Exact Overlap Characterization via Local Weak Convergence Extended Abstract. 4166-4167 - Omar Montasser, Abhishek Shetty, Nikita Zhivotovskiy:
Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks. 4168-4202 - Antoine Moulin, Gergely Neu, Luca Viano:
Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning. 4203-4270 - Manuel M. Müller, Yuetian Luo, Rina Foygel Barber:
Are all models wrong? Fundamental limits in distribution-free empirical model falsification. 4271-4308 - Cameron Musco, Christopher Musco, Lucas Rosenblatt, Apoorv Vikram Singh:
Sharper Bounds for Chebyshev Moment Matching, with Applications. 4309-4358 - Milind Nakul, Vidya Muthukumar, Ashwin Pananjady:
Estimating stationary mass, frequency by frequency. 4359 - Shyam Narayanan:
Improved algorithms for learning quantum Hamiltonians, via flat polynomials. 4360-4385 - Quan M. Nguyen, Shinji Ito, Junpei Komiyama, Nishant A. Mehta:
Data-dependent Bounds with T-Optimal Best-of-Both-Worlds Guarantees in Multi-Armed Bandits using Stability-Penalty Matching. 4386-4451 - Nataly Brukhim, Aldo Pacchiano, Miroslav Dudík, Robert E. Schapire:
On the Hardness of Bandit Learning. 4452-4485 - Hristo Papazov, Nicolas Flammarion:
Learning Algorithms in the Limit. 4486-4510 - Pan Peng, Hangyu Xu:
Differentially Private Synthetic Graphs Preserving Triangle-Motif Cuts. 4511-4564 - Emmanuel Pilliat:
Recovering Labels from Crowdsourced Data: an Optimal and Polynomial-Time Method. 4565-4595 - Thanasis Pittas, Ankit Pensia:
Optimal Robust Estimation under Local and Global Corruptions: Stronger Adversary and Smaller Error. 4596-4639 - Victor S. Portella, Nicholas J. A. Harvey:
Lower Bounds for Private Estimation of Gaussian Covariance Matrices under All Reasonable Parameter Regimes. 4640-4667 - Peter Potaptchik, Iskander Azangulov, George Deligiannidis:
Linear Convergence of Diffusion Models Under the Manifold Hypothesis. 4668-4685 - Mingda Qiao, Eric Zhao:
Truthfulness of Decision-Theoretic Calibration Measures. 4686-4739 - Vinod Raman, Jiaxun Li, Ambuj Tewari:
Generation through the lens of learning theory. 4740-4776 - Rahul Raychaudhury, Wen-Zhi Li, Syamantak Das, Sainyam Galhotra, Stavros Sintos:
Metric Clustering and Graph Optimization Problems using Weak Comparison Oracles. 4777-4830 - Dhruv Rohatgi, Adam Block, Audrey Huang, Akshay Krishnamurthy, Dylan J. Foster:
Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification (extended abstract). 4831-4837 - Dhruv Rohatgi, Dylan J. Foster:
Necessary and Sufficient Oracles: Toward a Computational Taxonomy for Reinforcement Learning. 4838-4936 - Raphael Rossellini, Jake A. Soloff, Rina Foygel Barber, Zhimei Ren, Rebecca Willett:
Can a calibration metric be both testable and actionable? 4937-4972 - Alexander Ryabchenko, Idan Attias, Daniel M. Roy:
Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs. 4973-5014 - Jongha Jon Ryu, Jeongyeol Kwon, Benjamin Koppe, Kwang-Sung Jun:
Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing. 5015-5053 - El Mehdi Saad, Wei-Cheng Lee, Francesco Orabona:
New Lower Bounds for Non-Convex Stochastic Optimization through Divergence Decomposition. 5054-5107 - Itay Safran, Daniel Reichman, Paul Valiant:
Depth Separations in Neural Networks: Separating the Dimension from the Accuracy. 5108-5142 - Sholom Schechtman, Nicolas Schreuder:
The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networks. 5143-5172 - Steve Hanneke, Shay Moran, Hilla Schefler, Iska Tsubari:
Private List Learnability vs. Online List Learnability. 5173-5213 - Xi Chen, William Pires, Toniann Pitassi, Rocco A. Servedio:
Testing Juntas and Junta Subclasses with Relative Error. 5214-5245 - Jan Seyfried, Sayantan Sen, Marco Tomamichel:
Testing (Conditional) Mutual Information - Extended Abstract. 5246-5247 - Max Simchowitz, Daniel Pfrommer, Ali Jadbabaie:
The title of the paper. 5248-5351 - Ludovic Stephan, Yizhe Zhu:
Community detection with the Bethe-Hessian. 5352-5353 - Dominik Stöger, Yizhe Zhu:
Non-convex matrix sensing: Breaking the quadratic rank barrier in the sample complexity Extended Abstract. 5354-5355 - Hadar Tal, Oron Sabag:
Optimal Online Bookmaking for Any Number of Outcomes. 5356-5409 - Chandan Tankala, Dheeraj Nagaraj, Anant Raj:
Beyond propagation of chaos: A stochastic algorithm for mean field optimization. 5410-5440 - Panos Tsimpos, Zhi Ren, Jakob Zech, Youssef Marzouk:
Optimal Scheduling of Dynamic Transport. 5441-5505 - Taira Tsuchiya, Shinji Ito, Haipeng Luo:
Corrupted Learning Dynamics in Games. 5506-5552 - Francisco Vasconcelos, Hsin-Yuan Huang:
Learning shallow quantum circuits with many-qubit gates. 5553-5604 - Yuanyu Wan:
Black-Box Reductions for Decentralized Online Convex Optimization in Changing Environments. 5605-5631 - Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D. Lee, Denny Wu:
Learning Compositional Functions with Transformers from Easy-to-Hard Data. 5632-5711 - Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu:
Orthogonal Causal Calibration (Extended Abstract). 5712-5713 - Justin Whitehouse, Zhiwei Steven Wu, Aaditya Ramdas:
Time-Uniform Self-Normalized Concentration for Vector-Valued Processes (Extended Abstract). 5714-5715 - Andre Wibisono:
Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality (Extended Abstract). 5716-5717 - Yu-Han Wu, Pierre Marion, Gérard Biau, Claire Boyer:
Taking a Big Step: Large Learning Rates in Denoising Score Matching Prevent Memorization. 5718-5756 - Yizhou Xu, Antoine Maillard, Lenka Zdeborová, Florent Krzakala:
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications. 5757-5823 - Konstantin Yakovlev, Nikita Puchkin:
Generalization error bound for denoising score matching under relaxed manifold assumption. 5824-5891 - Yichun Yang, Ronghua Li, Meihao Liao, Guoren Wang:
Improved Algorithms for Effective Resistance Computation on Graphs. 5892-5920 - Nikos Zarifis, Puqian Wang, Ilias Diakonikolas, Jelena Diakonikolas:
Robustly Learning Monotone Generalized Linear Models via Data Augmentation. 5921-5990 - Zihan Zhang, Jason D. Lee, Simon S. Du, Yuxin Chen:
Anytime Acceleration of Gradient Descent. 5991-6013 - Pengkun Yang, Jingzhao Zhang:
Fast and Multiphase Rates for Nearest Neighbor Classifiers. 6014-6015 - Raymond Zhang, Hédi Hadiji, Richard Combes:
Linear Bandits on Ellipsoids: Minimax Optimal Algorithms. 6016-6040 - Chicheng Zhang, Yihan Zhou:
Towards Fundamental Limits for Active Multi-distribution Learning. 6041-6090 - Huanjian Zhou, Andi Han, Akiko Takeda, Masashi Sugiyama:
The Adaptive Complexity of Finding a Stationary Point. 6091-6123 - Xiaohan Zhu, Nathan Srebro:
Quantifying Overfitting along the Regularization Path for Two-Part-Code MDL in Supervised Classification. 6124-6155 - Matthew Zurek, Yudong Chen:
Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for Average-Reward RL. 6156-6209 - Vincent Froese, Moritz Grillo, Christoph Hertrich, Martin Skutella:
Open Problem: Fixed-Parameter Tractability of Zonotope Problems. 6210-6214 - Yihong Gu:
Open Problem: Structure-Agnostic Minimax Risk for Partial Linear Model. 6220-6224 - Steve Hanneke, Shay Moran, Alexander Shlimovich, Amir Yehudayoff:
Open Problem: Data Selection for Regression Tasks. 6225-6229 - Arnab Maiti:
Open Problem: Optimal Instance-Dependent Sample Complexity for finding Nash Equilibrium in Two Player Zero-Sum Matrix games. 6230-6234
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