ICML 2019

773 papers

Unreproducible Research is Reproducible

Xavier Bouthillier, César Laurent, Pascal Vincent

Compositional Fairness Constraints for Graph Embeddings

Avishek Bose, William Hamilton

Online Variance Reduction with Mixtures

Zalán Borsos, Sebastian Curi, Kfir Yehuda Levy, Andreas Krause

Adversarial Attacks on Node Embeddings via Graph Poisoning

Aleksandar Bojchevski, Stephan Günnemann

Vinay Praneeth Boda, Prashanth L.A.

Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

Yochai Blau, Tomer Michaeli

A Kernel Perspective for Regularizing Deep Neural Networks

Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal

More Efficient Off-Policy Evaluation through Regularized Targeted Learning

Aurelien Bibaut, Ivana Malenica, Nikos Vlassis, Mark van der Laan

Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference

Yatao Bian, Joachim Buhmann, Andreas Krause

Analyzing Federated Learning through an Adversarial Lens

Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, Seraphin Calo

Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case

Alina Beygelzimer, Dávid Pál, Balázs Szörényi, Devanathan Thiruvenkatachari, Chen-Yu Wei

Adversarially Learned Representations for Information Obfuscation and Inference

Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues

Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning

Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger

Overcoming Multi-model Forgetting

Yassine Benyahia, Kaicheng Yu, Kamil Bennani Smires, Martin Jaggi, Anthony C. Davison

Greedy Layerwise Learning Can Scale To ImageNet

Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon

Invertible Residual Networks

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen

Active Learning for Probabilistic Structured Prediction of Cuts and Matchings

Sima Behpour, Anqi Liu, Brian Ziebart

Switching Linear Dynamics for Variational Bayes Filtering

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

Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces

Philipp Becker, Harit Pandya, Gregor Gebhardt, Cheng Zhao, C. James Taylor

Efficient optimization of loops and limits with randomized telescoping sums

Alex Beatson, Ryan P Adams

Noise2Self: Blind Denoising by Self-Supervision

Joshua Batson, Loic Royer

Categorical Feature Compression via Submodular Optimization

Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni

Pareto Optimal Streaming Unsupervised Classification

Soumya Basu, Steven Gutstein, Brent Lance, Sanjay Shakkottai

Scale-free adaptive planning for deterministic dynamics & discounted rewards

Peter Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko

A Personalized Affective Memory Model for Improving Emotion Recognition

Pablo Barros, German Parisi, Stefan Wermter

Learning to Route in Similarity Graphs

Dmitry Baranchuk, Dmitry Persiyanov, Anton Sinitsin, Artem Babenko

Structured agents for physical construction

Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly Stachenfeld, Pushmeet Kohli

HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving

Kshitij Bansal, Sarah Loos, Markus Rabe, Christian Szegedy, Stewart Wilcox

Concrete Autoencoders: Differentiable Feature Selection and Reconstruction

Muhammed Fatih Balın, Abubakar Abid, James Zou

Open-ended learning in symmetric zero-sum games

David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech Czarnecki, Julien Pérolat

Provable Guarantees for Gradient-Based Meta-Learning

Maria-Florina Balcan, Mikhail Khodak, Ameet Talwalkar

Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs

Yogesh Balaji, Hamed Hassani, Rama Chellappa, Soheil Feizi

Scalable Fair Clustering

Arturs Backurs, Piotr Indyk, Krzysztof Onak, Baruch Schieber, Ali Vakilian

Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior

Fadhel Ayed, Juho Lee, Francois Caron

Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data

Sergul Aydore, Bertrand Thirion, Gael Varoquaux

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA

Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra Slavković

Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations

Kubilay Atasu, Thomas Mittelholzer

Bayesian Optimization of Composite Functions

Raul Astudillo, Peter Frazier

Stochastic Gradient Push for Distributed Deep Learning

Mahmoud Assran, Nicolas Loizou, Nicolas Ballas, Mike Rabbat

Distributed Weighted Matching via Randomized Composable Coresets

Sepehr Assadi, Mohammadhossein Bateni, Vahab Mirrokni

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks

Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, Ruosong Wang

Unsupervised Label Noise Modeling and Loss Correction

Eric Arazo, Diego Ortego, Paul Albert, Noel O’Connor, Kevin McGuinness

Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data

Luigi Antelmi, Nicholas Ayache, Philippe Robert, Marco Lorenzi

Sorting Out Lipschitz Function Approximation

Cem Anil, James Lucas, Roger Grosse

Scaling Up Ordinal Embedding: A Landmark Approach

Jesse Anderton, Javed Aslam

Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation

Marco Ancona, Cengiz Oztireli, Markus Gross

Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy

Kareem Amin, Alex Kulesza, Andres Munoz, Sergei Vassilvtiskii

Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, Michael A. Osborne

A Convergence Theory for Deep Learning via Over-Parameterization

Zeyuan Allen-Zhu, Yuanzhi Li, Zhao Song

Infinite Mixture Prototypes for Few-shot Learning

Kelsey Allen, Evan Shelhamer, Hanul Shin, Joshua Tenenbaum

Analogies Explained: Towards Understanding Word Embeddings

Carl Allen, Timothy Hospedales

Graph Element Networks: adaptive, structured computation and memory

Ferran Alet, Adarsh Keshav Jeewajee, Maria Bauza Villalonga, Alberto Rodriguez, Tomás Lozano-Pérez

Multi-objective training of Generative Adversarial Networks with multiple discriminators

Isabela Albuquerque, Joao Monteiro, Thang Doan, Breandan Considine, Tiago Falk

Validating Causal Inference Models via Influence Functions

Ahmed Alaa, Mihaela van der Schaar

Projections for Approximate Policy Iteration Algorithms

Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann

Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search

Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito

Fairwashing: the risk of rationalization

Ulrich Aïvodji, Hiromi Arai, Olivier Fortineau, Sébastien Gambs, Satoshi Hara

Understanding the Impact of Entropy on Policy Optimization

Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions

Raj Agrawal, Brian Trippe, Jonathan Huggins, Tamara Broderick

Learning to Generalize from Sparse and Underspecified Rewards

Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

Fair Regression: Quantitative Definitions and Reduction-Based Algorithms

Alekh Agarwal, Miroslav Dudík, Zhiwei Steven Wu

Online Control with Adversarial Disturbances

Naman Agarwal, Brian Bullins, Elad Hazan, Sham Kakade, Karan Singh

Efficient Full-Matrix Adaptive Regularization

Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh

Static Automatic Batching In TensorFlow

Ashish Agarwal

PAC Learnability of Node Functions in Networked Dynamical Systems

Abhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikanti

TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning

Tameem Adel, Adrian Weller

Learning Models from Data with Measurement Error: Tackling Underreporting

Roy Adams, Yuelong Ji, Xiaobin Wang, Suchi Saria

Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters

Jayadev Acharya, Ziteng Sun

Distributed Learning with Sublinear Communication

Jayadev Acharya, Chris De Sa, Dylan Foster, Karthik Sridharan

Communication-Constrained Inference and the Role of Shared Randomness

Jayadev Acharya, Clement Canonne, Himanshu Tyagi

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman

Dynamic Weights in Multi-Objective Deep Reinforcement Learning

Axel Abels, Diederik Roijers, Tom Lenaerts, Ann Nowe, Denis Steckelmacher

AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs

Gabriele Abbati, Philippe Wenk, Michael A. Osborne, Andreas Krause, Bernhard Schölkopf