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ORCIDPKDD / ECML 2019: Würzburg, Germany
- Ulf Brefeld, Élisa Fromont
, Andreas Hotho, Arno J. Knobbe, Marloes H. Maathuis, Céline Robardet:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11907, Springer 2020, ISBN 978-3-030-46146-1
Supervised Learning
- Mike Izbicki, Evangelos E. Papalexakis, Vassilis J. Tsotras:
Exploiting the Earth's Spherical Geometry to Geolocate Images. 3-19 - Hung T. Nguyen, Xuejian Wang, Leman Akoglu:
Continual Rare-Class Recognition with Emerging Novel Subclasses. 20-36 - Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, Marcin Detyniecki:
Unjustified Classification Regions and Counterfactual Explanations in Machine Learning. 37-54 - Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull:
Shift Happens: Adjusting Classifiers. 55-70 - Jessa Bekker, Pieter Robberechts, Jesse Davis:
Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data. 71-85 - Ricardo B. C. Prudêncio:
Cost Sensitive Evaluation of Instance Hardness in Machine Learning. 86-102 - Mari-Liis Allikivi, Meelis Kull:
Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification. 103-120
Multi-label Learning
- Kai-wei Sun, Zijian Min, Jin Wang:
PP-PLL: Probability Propagation for Partial Label Learning. 123-137 - Jack Lanchantin, Arshdeep Sekhon, Yanjun Qi:
Neural Message Passing for Multi-label Classification. 138-163 - Laurence A. F. Park, Yi Guo, Jesse Read:
Assessing the Multi-labelness of Multi-label Data. 164-179 - Bin Liu, Grigorios Tsoumakas:
Synthetic Oversampling of Multi-label Data Based on Local Label Distribution. 180-193
Large-Scale Learning
- Mike Izbicki, Christian R. Shelton:
Distributed Learning of Non-convex Linear Models with One Round of Communication. 197-212 - Cong Xie, Oluwasanmi Koyejo, Indranil Gupta:
SLSGD: Secure and Efficient Distributed On-device Machine Learning. 213-228 - Robin Vogel, Aurélien Bellet, Stéphan Clémençon, Ons Jelassi, Guillaume Papa:
Trade-Offs in Large-Scale Distributed Tuplewise Estimation And Learning. 229-245
Deep Learning
- Maurice Diesendruck, Ethan R. Elenberg, Rajat Sen, Guy W. Cole, Sanjay Shakkottai, Sinead A. Williamson:
Importance Weighted Generative Networks. 249-265 - Takuro Kutsuna:
Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks. 266-282 - Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori:
LYRICS: A General Interface Layer to Integrate Logic Inference and Deep Learning. 283-298 - Lena A. Jäger, Silvia Makowski, Paul Prasse, Sascha Liehr, Maximilian Seidler, Tobias Scheffer:
Deep Eyedentification: Biometric Identification Using Micro-movements of the Eye. 299-314 - Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo:
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization. 315-331 - Ahmed AbdelWahab, Niels Landwehr:
Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics. 332-348 - Marco Frasca, Giuliano Grossi, Giorgio Valentini:
Multitask Hopfield Networks. 349-365 - Vishnu TV, Pankaj Malhotra, Jyoti Narwariya, Lovekesh Vig, Gautam Shroff:
Meta-Learning for Black-Box Optimization. 366-381 - Wolfgang Roth, Günther Schindler, Holger Fröning, Franz Pernkopf:
Training Discrete-Valued Neural Networks with Sign Activations Using Weight Distributions. 382-398 - Matthias Kissel, Klaus Diepold:
Sobolev Training with Approximated Derivatives for Black-Box Function Regression with Neural Networks. 399-414 - Nico Piatkowski:
Hyper-Parameter-Free Generative Modelling with Deep Boltzmann Trees. 415-431 - Yang Li, Shihao Ji:
L0-ARM: Network Sparsification via Stochastic Binary Optimization. 432-448 - Léonard Blier, Pierre Wolinski, Yann Ollivier:
Learning with Random Learning Rates. 449-464 - Ali Caner Türkmen, Yuyang Wang, Alexander J. Smola:
FastPoint: Scalable Deep Point Processes. 465-480 - Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, Diana Marculescu:
Single-Path NAS: Designing Hardware-Efficient ConvNets in Less Than 4 Hours. 481-497
Probabilistic Models
- Martin Wistuba, Ambrish Rawat:
Scalable Large Margin Gaussian Process Classification. 501-516 - Giuseppe Marra, Francesco Giannini, Michelangelo Diligenti, Marco Gori:
Integrating Learning and Reasoning with Deep Logic Models. 517-532 - Ruosi Wan, Mingjun Zhong, Haoyi Xiong, Zhanxing Zhu:
Neural Control Variates for Monte Carlo Variance Reduction. 533-547 - Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Data Association with Gaussian Processes. 548-564 - Kai Chen, Twan van Laarhoven, Jinsong Chen, Elena Marchiori:
Incorporating Dependencies in Spectral Kernels for Gaussian Processes. 565-581 - Kenneth Blomqvist, Samuel Kaski, Markus Heinonen:
Deep Convolutional Gaussian Processes. 582-597 - Daniel F. Schmidt, Enes Makalic:
Bayesian Generalized Horseshoe Estimation of Generalized Linear Models. 598-613 - Khan Mohammad Al Farabi, Somdeb Sarkhel, Sanorita Dey, Deepak Venugopal:
Fine-Grained Explanations Using Markov Logic. 614-629
Natural Language Processing
- Taesung Lee, Youngja Park:
Unsupervised Sentence Embedding Using Document Structure-Based Context. 633-647 - Marco Roberti, Giovanni Bonetta, Rossella Cancelliere, Patrick Gallinari:
Copy Mechanism and Tailored Training for Character-Based Data-to-Text Generation. 648-664 - Shobeir Fakhraei, Joel Mathew, José Luis Ambite:
NSEEN: Neural Semantic Embedding for Entity Normalization. 665-680 - Maarten Grootendorst, Joaquin Vanschoren:
Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts. 681-696 - Cornelia Ferner, Stefan Wegenkittl:
A Semi-discriminative Approach for Sub-sentence Level Topic Classification on a Small Dataset. 697-710 - Prashanth Vijayaraghavan, Deb Roy:
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model. 711-726
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