ICML

Random Function Priors for Correlation Modeling

Aonan Zhang, John Paisley

Comments Accepted by ICML 2019. 13 pages, 6 figures

Submodular Streaming in All its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity

Ehsan Kazemi, Marko Mitrovic, Morteza Zadimoghaddam, Silvio Lattanzi, Amin Karbasi

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

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli

Comments Accepted as a conference paper at ICML 2019

Structured agents for physical construction

Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick

Comments ICML 2019

Target Tracking for Contextual Bandits: Application to Demand Side Management

Margaux Brégère, Pierre Gaillard, Yannig Goude, Gilles Stoltz

Journal ref ICML 2019 (Thirty-sixth International Conference on Machine Learning), Jun 2019, Long Beach, United States

Open-ended Learning in Symmetric Zero-sum Games

David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel

Comments ICML 2019, final version

Online Adaptive Principal Component Analysis and Its extensions

Jianjun Yuan, Andrew Lamperski

Comments This paper is accepted by ICML 2019

Rao-Blackwellized Stochastic Gradients for Discrete Distributions

Runjing Liu, Jeffrey Regier, Nilesh Tripuraneni, Michael I. Jordan, Jon McAuliffe

Comments Accepted to ICML 2019

Manifold Mixup: Better Representations by Interpolating Hidden States

Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, Aaron Courville, David Lopez-Paz, Yoshua Bengio

Comments To appear in ICML 2019

Imitating Latent Policies from Observation

Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker, Charles L. Isbell

Comments Accepted to ICML 2019

CoT: Cooperative Training for Generative Modeling of Discrete Data

Sidi Lu, Lantao Yu, Siyuan Feng, Yaoming Zhu, Weinan Zhang, Yong Yu

Comments Appearing as a Conference Paper on ICML 2019

Global Convergence of Block Coordinate Descent in Deep Learning

Jinshan Zeng, Tim Tsz-Kit Lau, Shaobo Lin, Yuan Yao

Comments 27 pages, 2 figures

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

On the Connection Between Adversarial Robustness and Saliency Map Interpretability

Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb

Comments 12 pages, accepted for publication at the 36th International Conference on Machine Learning 2019

Single-Path NAS: Device-Aware Efficient ConvNet Design

Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, Diana Marculescu

Comments ODML-CDNNR 2019 (ICML'19 workshop) oral presentation (extended abstract, required non-archival version). Full paper: arXiv:1904.02877

Refined Complexity of PCA with Outliers

Fedor V. Fomin, Petr A. Golovach, Fahad Panolan, Kirill Simonov

Comments To be presented at ICML 2019

On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization

Hao Yu, Rong Jin, Sen Yang

Comments A short version of this paper is accepted to ICML 2019

Understanding Priors in Bayesian Neural Networks at the Unit Level

Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, Julyan Arbel

Comments 10 pages, 5 figures, ICML'19 conference

Importance Sampling Policy Evaluation with an Estimated Behavior Policy

Josiah P. Hanna, Scott Niekum, Peter Stone

Comments Accepted to ICML 2019

Gaining Free or Low-Cost Transparency with Interpretable Partial Substitute

Tong Wang

Journal ref International Conference on Machine Learning, 2019

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study

Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith

Comments 17 pages, 3 tables, 17 figures; accepted to ICML 2019

Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization

Baojian Zhou, Feng Chen, Yiming Ying

Comments published in ICML-2019

Journal ref Proceedings of the 36th International Conference on Machine Learning, 2019

Does Data Augmentation Lead to Positive Margin?

Shashank Rajput, Zhili Feng, Zachary Charles, Po-Ling Loh, Dimitris Papailiopoulos

Comments ICML 2019

Training Neural Networks with Local Error Signals

Arild Nøkland, Lars Hiller Eidnes

Comments Accepted to ICML 2019

Estimate Sequences for Variance-Reduced Stochastic Composite Optimization

Andrei Kulunchakov, Julien Mairal

Comments short version of preprint arXiv:1901.08788

Journal ref International Conference on Machine Learning (ICML), Jun 2019, Long Beach, United States

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Romain Lopez, Achille Nazaret, Maxime Langevin, Jules Samaran, Jeffrey Regier, Michael I. Jordan, Nir Yosef

Comments submitted to the 2019 ICML Workshop on Computational Biology

Action Robust Reinforcement Learning and Applications in Continuous Control

Chen Tessler, Yonathan Efroni, Shie Mannor

Comments Accepted to ICML 2019

Understanding Geometry of Encoder-Decoder CNNs

Jong Chul Ye, Woon Kyoung Sung

Comments Accepted to ICML 2019

Gromov-Wasserstein Learning for Graph Matching and Node Embedding

Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin

Journal ref Thirty-sixth International Conference on Machine Learning 2019

A Persona-based Multi-turn Conversation Model in an Adversarial Learning Framework

Oluwatobi O. Olabiyi, Anish Khazane, Erik T. Mueller

Comments 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). arXiv admin note: substantial text overlap with arXiv:1905.01992

Deep Generative Learning via Variational Gradient Flow

Yuan Gao, Yuling Jiao, Yang Wang, Yao Wang, Can Yang, Shunkang Zhang

Journal ref ICML 2019