ICML

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

Journal ref Proceedings of the 36th International Conference on Machine Learning (ICML 2019)

Hierarchical Soft Actor-Critic: Adversarial Exploration via Mutual Information Optimization

Ari Azarafrooz, John Brock

Comments Presented at the ICML 2019 workshop on Imitation, Intent, and Interaction, Long Beach, CA, USA

Normalizing flows for novelty detection in industrial time series data

Maximilian Schmidt, Marko Simic

Comments Presented at "First workshop on Invertible Neural Networks and Normalizing Flows(ICML 2019), Long Beach, CA, USA"

Recovering the parameters underlying the Lorenz-96 chaotic dynamics

Soukayna Mouatadid, Pierre Gentine, Wei Yu, Steve Easterbrook

Comments ICML 2019 workshop on climate change

Multi-scale Embedded CNN for Music Tagging (MsE-CNN)

Nima Hamidi, Mohsen Vahidzadeh, Stephen Baek

Comments Proceedings of the 36th International Conference on Machine Learning (ICML)

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

Tom Beucler, Stephan Rasp, Michael Pritchard, Pierre Gentine

Comments ICML 2019 Workshop. Climate Change: How Can AI Help? 3 pages, 3 figures, 1 table

User Curated Shaping of Expressive Performances

Zhengshan Shi, Carlos Cancino-Chacón, Gerhard Widmer

Comments 4 pages, ICML 2019 Machine Learning for Music Discovery Workshop

Extracting Interpretable Concept-Based Decision Trees from CNNs

Conner Chyung, Michael Tsang, Yan Liu

Comments presented at 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA

Certified Adversarial Robustness via Randomized Smoothing

Jeremy M Cohen, Elan Rosenfeld, J. Zico Kolter

Comments ICML 2019

Policy Consolidation for Continual Reinforcement Learning

Christos Kaplanis, Murray Shanahan, Claudia Clopath

Comments Accepted at ICML 2019

Mallows Ranking Models: Maximum Likelihood Estimate and Regeneration

Wenpin Tang

Comments 10 pages, 2 figures, 5 tables. This paper is published by http://proceedings.mlr.press/v97/tang19a.html

Journal ref Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6125-6134

Metric-Optimized Example Weights

Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, Maya Gupta

Comments Proceedings of the 36th International Conference on Machine Learning (ICML'19)

First-order Adversarial Vulnerability of Neural Networks and Input Dimension

Carl-Johann Simon-Gabriel, Yann Ollivier, Léon Bottou, Bernhard Schölkopf, David Lopez-Paz

Comments Paper previously called: "Adversarial Vulnerability of Neural Networks Increases with Input Dimension". 9 pages main text and references, 11 pages appendix, 14 figures

Journal ref Proceedings of ICML 2019

GluonTS: Probabilistic Time Series Models in Python

Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

Comments ICML Time Series Workshop 2019

Position-aware Graph Neural Networks

Jiaxuan You, Rex Ying, Jure Leskovec

Comments ICML 2019, long oral

Model-Based Active Exploration

Pranav Shyam, Wojciech Jaśkowski, Faustino Gomez

Comments ICML 2019. Code: https://github.com/nnaisense/max

SmartChoices: Hybridizing Programming and Machine Learning

Victor Carbune, Thierry Coppey, Alexander Daryin, Thomas Deselaers, Nikhil Sarda, Jay Yagnik

Comments published at the Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning (ICML), Long Beach, California, USA, 2019

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

Comments 10 pages, 4 figures, 3 tables. Published in the proceedings of the Thirty-sixth International Conference on Machine Learning (ICML), 2019

Journal ref PMLR 97:2931-2940 (2019)

Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning

Quanying Liu, Haiyan Wu, Anqi Liu

Comments Real-world Sequential Decision Making Workshop at ICML 2019

Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation

Erik Englesson, Hossein Azizpour

Comments Submitted at the ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning(poster & spotlight talk)

Self-Attention Graph Pooling

Junhyun Lee, Inyeop Lee, Jaewoo Kang

Comments 10 pages, 3 figures, 4 tables. Accepted to ICML 2019

EMI: Exploration with Mutual Information

Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song

Comments Accepted and to appear at ICML 2019

Multiple instance learning with graph neural networks

Ming Tu, Jing Huang, Xiaodong He, Bowen Zhou

Comments Accepted to ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Representations

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics

Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva

Comments Published as a workshop paper on AI in Finance: Applications and Infrastructure for Multi-Agent Learning at the 36th International Conference on Machine Learning (ICML), Long Beach, California, PMLR97, 2019. Copyright 2019 by the author(s)

Issues with post-hoc counterfactual explanations: a discussion

Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Marcin Detyniecki

Comments presented at 2019 ICML Workshop on Human in the Loop Learning (HILL 2019), Long Beach, USA

Extending Deep Learning Models for Limit Order Books to Quantile Regression

Zihao Zhang, Stefan Zohren, Stephen Roberts

Comments 5 pages, 4 figures, Time Series Workshop of the ICML (2019)

Journal ref Proceedings of Time Series Workshop of the 36 th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019

Adaptively Preconditioned Stochastic Gradient Langevin Dynamics

Chandrasekaran Anirudh Bhardwaj

Comments International Conference on Machine Learning (ICML) 2019 Workshop on Understanding and Improving Generalization in Deep Learning

Minimal Achievable Sufficient Statistic Learning

Milan Cvitkovic, Günther Koliander

Comments Published in the International Conference on Machine Learning (ICML 2019), 23 pages

Disentangling Disentanglement in Variational Autoencoders

Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh

Comments Accepted for publication at ICML 2019

Learning Symmetries of Classical Integrable Systems

Roberto Bondesan, Austen Lamacraft

Comments 8 pages, 5 figures. Presented at the ICML 2019 Workshop on Theoretical Physics for Deep Learning