ICML

Multi-Agent Adversarial Inverse Reinforcement Learning

Lantao Yu, Jiaming Song, Stefano Ermon

Comments ICML 2019

Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance

Vinay Uday Prabhu, Dian Ang Yap, Alexander Wang, John Whaley

Comments Accepted for presentation at the first workshop on Invertible Neural Networks and Normalizing Flows (ICML 2019), Long Beach, CA, USA

Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

Yochai Blau, Tomer Michaeli

Comments ICML 2019 (long oral presentation), see talk at: https://slideslive.com/38917633/applications-computer-vision

Journal ref Proceedings of the 36th International Conference on Machine Learning, PMLR 97:675-685, 2019

Learning to Groove with Inverse Sequence Transformations

Jon Gillick, Adam Roberts, Jesse Engel, Douglas Eck, David Bamman

Comments Blog post and links: https://g.co/magenta/groovae

Journal ref Proceedings of the 36th International Conference on Machine Learning, PMLR 97:2269-2279, 2019

Towards AutoML in the presence of Drift: first results

Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, Wei-Wei Tu, Yang Yu, Lisheng Sun-Hosoya, Isabelle Guyon, Michele Sebag

Comments AutoML 2018 @ ICML/IJCAI-ECAI

Deep Generative Quantile-Copula Models for Probabilistic Forecasting

Ruofeng Wen, Kari Torkkola

Comments Published at the 36th International Conference on Machine Learning (ICML2019), Time Series Workshop, Long Beach, California, 2019

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

Samuel Wiqvist, Pierre-Alexandre Mattei, Umberto Picchini, Jes Frellsen

Comments Forthcoming on the Proceedings of ICML 2019. New comparisons with several different networks. We now use the Wasserstein distance to produce comparisons. Code available on GitHub. 16 pages, 5 figures, 21 tables

Journal ref Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6798--6807, 2019

Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients

Zhenwei Dai, Reinhard Heckel

Comments 13 pages, 5 figures

Journal ref ICML 2019 Workshop Deep Phenomena

Bridging Theory and Algorithm for Domain Adaptation

Yuchen Zhang, Tianle Liu, Mingsheng Long, Michael I. Jordan

Comments Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019

Understanding Adversarial Robustness Through Loss Landscape Geometries

Vinay Uday Prabhu, Dian Ang Yap, Joyce Xu, John Whaley

Comments Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning, and CVPR 2019 Workshop on The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security (CV-COPS)

Similarity of Neural Network Representations Revisited

Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton

Comments ICML 2019

PAN: Path Integral Based Convolution for Deep Graph Neural Networks

Zheng Ma, Ming Li, Yuguang Wang

Journal ref ICML 2019 Workshop on Learning and Reasoning with Graph-Structured Representations (Oral)

Amortized Monte Carlo Integration

Adam Goliński, Frank Wood, Tom Rainforth

Comments Awarded Best Paper Honourable Mention at International Conference on Machine Learning (ICML) 2019

Homomorphic Sensing

Manolis C. Tsakiris, Liangzu Peng

Journal ref Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6335-6344, 2019

Theoretical Analysis of Sparse Subspace Clustering with Missing Entries

Manolis C. Tsakiris, Rene Vidal

Journal ref Proceedings of the 35th International Conference on Machine Learning, PMLR 80:4975-4984, 2018

Hyperplane Clustering Via Dual Principal Component Pursuit

Manolis C. Tsakiris, Rene Vidal

Journal ref Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3472-3481, 2017

Self Organizing Supply Chains for Micro-Prediction: Present and Future uses of the ROAR Protocol

Peter Cotton

Comments Thirty-sixth International Conference on Machine Learning Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning

Compositional Fairness Constraints for Graph Embeddings

Avishek Joey Bose, William L. Hamilton

Comments Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019

Processing Megapixel Images with Deep Attention-Sampling Models

Angelos Katharopoulos, François Fleuret

Comments Presented in ICML 2019. Code is available at https://github.com/idiap/attention-sampling

Journal ref Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3282-3291, 2019

How much real data do we actually need: Analyzing object detection performance using synthetic and real data

Farzan Erlik Nowruzi, Prince Kapoor, Dhanvin Kolhatkar, Fahed Al Hassanat, Robert Laganiere, Julien Rebut

Comments Accepted in International Conference on Machine Learning (ICML 2019) Workshop on AI for Autonomous Driving

Investigating Channel Pruning through Structural Redundancy Reduction -- A Statistical Study

Chengcheng Li, Zi Wang, Dali Wang, Xiangyang Wang, Hairong Qi

Comments 2019 ICML Workshop, Joint Workshop on On-Device Machine Learning & Compact Deep Neural Network Representations (ODML-CDNNR)

Exploring Deep Anomaly Detection Methods Based on Capsule Net

Xiaoyan Li, Iluju Kiringa, Tet Yeap, Xiaodan Zhu, Yifeng Li

Comments Presented in the "ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning", June 14, Long Beach, California, USA

Classification from Positive, Unlabeled and Biased Negative Data

Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama

Comments In Proceedings of the 36th International Conference on Machine Learning (ICML 2019)

Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction

Vikash Singh, Pietro Lio'

Comments Workshop on Computational Biology (WCB) at ICML 2019

Task-Agnostic Dynamics Priors for Deep Reinforcement Learning

Yilun Du, Karthik Narasimhan

Comments ICML 2019

Interactive Topic Modeling with Anchor Words

Sanjoy Dasgupta, Stefanos Poulis, Christopher Tosh

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

Improved Convergence for $\ell_\infty$ and $\ell_1$ Regression via Iteratively Reweighted Least Squares

Alina Ene, Adrian Vladu

Comments Appears in ICML 2019

Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum

Comments In proceedings of Thirty-sixth International Conference on Machine Learning (ICML 2019)

Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching

Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, Liang Lin

Comments ICML-19

Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm

Piotr Indyk, Sepideh Mahabadi, Shayan Oveis Gharan, Alireza Rezaei

Comments This paper has appeared in the 36th International Conference on Machine Learning (ICML), 2019. This is an equal contribution paper