ICML 2019

773 papers

SGD without Replacement: Sharper Rates for General Smooth Convex Functions

Dheeraj Nagaraj, Prateek Jain, Praneeth Netrapalli

A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning

Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama

Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models

Mor Shpigel Nacson, Suriya Gunasekar, Jason Lee, Nathan Srebro, Daniel Soudry

Learning Optimal Fair Policies

Razieh Nabi, Daniel Malinsky, Ilya Shpitser

Relational Pooling for Graph Representations

Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro

A Dynamical Systems Perspective on Nesterov Acceleration

Michael Muehlebach, Michael Jordan

Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization

Hesham Mostafa, Xin Wang

Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization

Seungyong Moon, Gaon An, Hyun Oh Song

Flat Metric Minimization with Applications in Generative Modeling

Thomas Möllenhoff, Daniel Cremers

Agnostic Federated Learning

Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh

Co-manifold learning with missing data

Gal Mishne, Eric Chi, Ronald Coifman

Formal Privacy for Functional Data with Gaussian Perturbations

Ardalan Mirshani, Matthew Reimherr, Aleksandra Slavković

Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography

Andrew Miller, Ziad Obermeyer, John Cunningham, Sendhil Mullainathan

On Dropout and Nuclear Norm Regularization

Poorya Mianjy, Raman Arora

Optimality Implies Kernel Sum Classifiers are Statistically Efficient

Raphael Meyer, Jean Honorio

Understanding and correcting pathologies in the training of learned optimizers

Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, Jascha Sohl-dickstein

Reinforcement Learning in Configurable Continuous Environments

Alberto Maria Metelli, Emanuele Ghelfi, Marcello Restelli

Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization

Michael Metel, Akiko Takeda

Spectral Clustering of Signed Graphs via Matrix Power Means

Pedro Mercado, Francesco Tudisco, Matthias Hein

Geometric Losses for Distributional Learning

Arthur Mensch, Mathieu Blondel, Gabriel Peyré

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

Charith Mendis, Alex Renda, Dr.Saman Amarasinghe, Michael Carbin

The Wasserstein Transform

Facundo Mémoli, Zane Smith, Zhengchao Wan

Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization

Eldad Meller, Alexander Finkelstein, Uri Almog, Mark Grobman

Imputing Missing Events in Continuous-Time Event Streams

Hongyuan Mei, Guanghui Qin, Jason Eisner

Stochastic Blockmodels meet Graph Neural Networks

Nikhil Mehta, Lawrence Carin Duke, Piyush Rai

Toward Controlling Discrimination in Online Ad Auctions

Elisa Celis, Anay Mehrotra, Nisheeth Vishnoi

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

Geoffrey Roeder, Paul Grant, Andrew Phillips, Neil Dalchau, Edward Meeds

Graphical-model based estimation and inference for differential privacy

Ryan McKenna, Daniel Sheldon, Gerome Miklau

Distributional Reinforcement Learning for Efficient Exploration

Borislav Mavrin, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets

Pierre-Alexandre Mattei, Jes Frellsen

Disentangling Disentanglement in Variational Autoencoders

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

Optimal Minimal Margin Maximization with Boosting

Alexander Mathiasen, Kasper Green Larsen, Allan Grønlund

Fairness-Aware Learning for Continuous Attributes and Treatments

Jeremie Mary, Clément Calauzènes, Noureddine El Karoui

Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models

Kaspar Märtens, Kieran Campbell, Christopher Yau

On the Universality of Invariant Networks

Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman

Adversarial Generation of Time-Frequency Features with application in audio synthesis

Andrés Marafioti, Nathanael Perraudin, Nicki Holighaus, Piotr Majdak

A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs

Jingkai Mao, Jakob Foerster, Tim Rocktäschel, Maruan Al-Shedivat, Gregory Farquhar

Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models

Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems

Timothy Arthur Mann, Sven Gowal, András György, Huiyi Hu, Ray Jiang

Calibrated Model-Based Deep Reinforcement Learning

Ali Malik, Volodymyr Kuleshov, Jiaming Song, Danny Nemer, Harlan Seymour

Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms

Ashok Makkuva, Pramod Viswanath, Sreeram Kannan, Sewoong Oh

Curvature-Exploiting Acceleration of Elastic Net Computations

Vien Mai, Mikael Johansson

Traditional and Heavy Tailed Self Regularization in Neural Network Models

Michael Mahoney, charles martin

Universal Multi-Party Poisoning Attacks

Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed

Guided evolutionary strategies: augmenting random search with surrogate gradients

Niru Maheswaranathan, Luke Metz, George Tucker, Dami Choi, Jascha Sohl-dickstein

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

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

Bayesian leave-one-out cross-validation for large data

Måns Magnusson, Michael Andersen, Johan Jonasson, Aki Vehtari

EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE

Chao Ma, Sebastian Tschiatschek, Konstantina Palla, José Miguel Hernández-Lobato, Sebastian Nowozin

Variational Implicit Processes

Chao Ma, Yingzhen Li, José Miguel Hernández-Lobato

Disentangled Graph Convolutional Networks

Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu

Differentiable Dynamic Normalization for Learning Deep Representation

Ping Luo, Peng Zhanglin, Shao Wenqi, Zhang Ruimao, Ren Jiamin

Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction

Giulia Luise, Dimitrios Stamos, Massimiliano Pontil, Carlo Ciliberto

High-Fidelity Image Generation With Fewer Labels

Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem

Generalized Approximate Survey Propagation for High-Dimensional Estimation

Carlo Lucibello, Luca Saglietti, Yue Lu

CoT: Cooperative Training for Generative Modeling of Discrete Data

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

Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards

Shiyin Lu, Guanghui Wang, Yao Hu, Lijun Zhang

Neurally-Guided Structure Inference

Sidi Lu, Jiayuan Mao, Joshua Tenenbaum, Jiajun Wu

PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization

Songtao Lu, Mingyi Hong, Zhengdao Wang

Bayesian Counterfactual Risk Minimization

Ben London, Ted Sandler

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions

Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter

Understanding MCMC Dynamics as Flows on the Wasserstein Space

Chang Liu, Jingwei Zhuo, Jun Zhu

Understanding and Accelerating Particle-Based Variational Inference

Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu

On Certifying Non-Uniform Bounds against Adversarial Attacks

Chen Liu, Ryota Tomioka, Volkan Cevher

Taming MAML: Efficient unbiased meta-reinforcement learning

Hao Liu, Richard Socher, Caiming Xiong

The Implicit Fairness Criterion of Unconstrained Learning

Lydia T. Liu, Max Simchowitz, Moritz Hardt

Data Poisoning Attacks on Stochastic Bandits

Fang Liu, Ness Shroff

Sparse Extreme Multi-label Learning with Oracle Property

Weiwei Liu, Xiaobo Shen

Rao-Blackwellized Stochastic Gradients for Discrete Distributions

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

Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers

Hong Liu, Mingsheng Long, Jianmin Wang, Michael Jordan

Acceleration of SVRG and Katyusha X by Inexact Preconditioning

Yanli Liu, Fei Feng, Wotao Yin

Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations

Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt

On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms

Tianyi Lin, nhật Hồ, Michael Jordan

Kernel-Based Reinforcement Learning in Robust Markov Decision Processes

Shiau Hong Lim, Arnaud Autef

Inference and Sampling of K_33-free Ising Models

Valerii Likhosherstov, Yury Maximov, Misha Chertkov

Regularization in directable environments with application to Tetris

Jan Malte Lichtenberg, Ozgur Simsek

Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints

Nikolaos Liakopoulos, Apostolos Destounis, Georgios Paschos, Thrasyvoulos Spyropoulos, Panayotis Mertikopoulos

Alternating Minimizations Converge to Second-Order Optimal Solutions

Qiuwei Li, Zhihui Zhu, Gongguo Tang

Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting

Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong

Feature-Critic Networks for Heterogeneous Domain Generalization

Yiying Li, Yongxin Yang, Wei Zhou, Timothy Hospedales

Towards a Unified Analysis of Random Fourier Features

Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic

Adversarial camera stickers: A physical camera-based attack on deep learning systems

Juncheng Li, Frank Schmidt, Zico Kolter

Exploiting Worker Correlation for Label Aggregation in Crowdsourcing

Yuan Li, Benjamin Rubinstein, Trevor Cohn

Bayesian Joint Spike-and-Slab Graphical Lasso

Zehang Li, Tyler Mccormick, Samuel Clark

NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks

Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, Boqing Gong

Online Learning to Rank with Features

Shuai Li, Tor Lattimore, Csaba Szepesvári

Area Attention

Yang Li, LUKASZ KAISER, Samy Bengio, Si Si

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

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

LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

Huaiyu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang

Sublinear quantum algorithms for training linear and kernel-based classifiers

Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu

Are Generative Classifiers More Robust to Adversarial Attacks?

Yingzhen Li, John Bradshaw, Yash Sharma

Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group

Mario Lezcano-Casado, David Martı́nez-Rubio

MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means

Matthieu Lerasle, Zoltan Szabo, Timothée Mathieu, Guillaume Lecue

Sublinear Time Nearest Neighbor Search over Generalized Weighted Space

Yifan Lei, Qiang Huang, Mohan Kankanhalli, Anthony Tung

Robust Inference via Generative Classifiers for Handling Noisy Labels

Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li

First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems

Ching-pei Lee, Stephen Wright

Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi

Self-Attention Graph Pooling

Junhyun Lee, Inyeop Lee, Jaewoo Kang

Functional Transparency for Structured Data: a Game-Theoretic Approach

Guang-He Lee, Wengong Jin, David Alvarez-Melis, Tommi Jaakkola

Target-Based Temporal-Difference Learning

Donghwan Lee, Niao He