ICML

Matrix-Free Preconditioning in Online Learning

Ashok Cutkosky, Tamas Sarlos

Comments ICML 2019

Path-Augmented Graph Transformer Network

Benson Chen, Regina Barzilay, Tommi Jaakkola

Comments Appears in ICML LRG Workshop

On the Generalization Gap in Reparameterizable Reinforcement Learning

Huan Wang, Stephan Zheng, Caiming Xiong, Richard Socher

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

Learning latent state representation for speeding up exploration

Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, Pieter Abbeel

Comments 7 pages, 8 figures, workshop

Journal ref 2nd Exploration in Reinforcement Learning Workshop at the 36 th International Conference on Machine Learning, 2019

Hijacking Malaria Simulators with Probabilistic Programming

Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atılım Güneş Baydin

Comments 6 pages, 3 figures, Accepted at the International Conference on Machine Learning AI for Social Good Workshop, Long Beach, United States, 2019

Journal ref ICML Workshop on AI for Social Good, 2018

Batch Normalization is a Cause of Adversarial Vulnerability

Angus Galloway, Anna Golubeva, Thomas Tanay, Medhat Moussa, Graham W. Taylor

Comments To appear in the ICML 2019 Workshop on Identifying and Understanding Deep Learning Phenomena

Unsupervised Deep Learning by Neighbourhood Discovery

Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu

Comments 36th International Conference on Machine Learning (ICML'19). Code is available at https://github.com/Raymond-sci/AND

Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement

Wouter Kool, Herke van Hoof, Max Welling

Comments ICML 2019 ; 13 pages, 4 figures

Estimating Information Flow in Deep Neural Networks

Ziv Goldfeld, Ewout van den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, Yury Polyanskiy

Comments Main text accepted to ICML 2019. This preprint contains the full version of that paper (including omitted appendices)

Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks

Charith Mendis, Alex Renda, Saman Amarasinghe, Michael Carbin

Comments Published at 36th International Conference on Machine Learning (ICML) 2019

Journal ref Proceedings of Machine Learning Research - Volume 97 (ICML 2019)

Sever: A Robust Meta-Algorithm for Stochastic Optimization

Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart

Comments To appear in ICML 2019

Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems

Gokce Keskin, Tyler Lee, Cory Stephenson, Oguz H. Elibol

Comments Accepted for publication at ICML 2019 Synthetic Realities: Workshop on Detecting Audio-Visual Fakes

Switching Linear Dynamics for Variational Bayes Filtering

Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt

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

Deep Factors for Forecasting

Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski

Comments http://proceedings.mlr.press/v97/wang19k/wang19k.pdf. arXiv admin note: substantial text overlap with arXiv:1812.00098

Journal ref Proceedings of Machine Learning Research, Volume 97: International Conference on Machine Learning, 2019

Discovering Conditionally Salient Features with Statistical Guarantees

Jaime Roquero Gimenez, James Zou

Comments Accepted at ICML 2019

SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver

Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter

Comments Accepted at ICML'19. The code can be found at https://github.com/locuslab/satnet

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

Tian Guo, Tao Lin, Nino Antulov-Fantulin

Comments Accepted to International Conference on Machine Learning (ICML), 2019

Bayesian Nonparametric Federated Learning of Neural Networks

Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, Yasaman Khazaeni

Comments ICML 2019

Optimisation of Overparametrized Sum-Product Networks

Martin Trapp, Robert Peharz, Franz Pernkopf

Comments Workshop on Tractable Probabilistic Models (TPM) at ICML 2019

Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning

Seungyul Han, Youngchul Sung

Comments Accepted to the 36th International Conference on Machine Learning (ICML), 2019

Hybrid Models with Deep and Invertible Features

Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan

Comments ICML 2019

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

Sai Praneeth Karimireddy, Quentin Rebjock, Sebastian U. Stich, Martin Jaggi

Comments ICML 2019 (long talk)

Improving Adversarial Robustness via Promoting Ensemble Diversity

Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu

Comments ICML 2019

Gradient Descent Finds Global Minima of Deep Neural Networks

Simon S. Du, Jason D. Lee, Haochuan Li, Liwei Wang, Xiyu Zhai

Comments ICML 2019

Dropout as a Structured Shrinkage Prior

Eric Nalisnick, José Miguel Hernández-Lobato, Padhraic Smyth

Comments ICML 2019

Direct Uncertainty Prediction for Medical Second Opinions

Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Robert Kleinberg, Sendhil Mullainathan, Jon Kleinberg

Comments Accepted for publication at ICML 2019

Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications

Pin-Yu Chen, Lingfei Wu, Sijia Liu, Indika Rajapakse

Comments Published at ICML 2019

ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

Yuzhe Yang, Guo Zhang, Dina Katabi, Zhi Xu

Comments ICML 2019

Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters

Jayadev Acharya, Ziteng Sun

Comments ICML 2019

On Dropout and Nuclear Norm Regularization

Poorya Mianjy, Raman Arora

Journal ref Proceedings of the 36th International Conference on Machine Learning, PMLR 97:4575-4584, 2019