ICML 2019

773 papers

Batch Policy Learning under Constraints

Hoang Le, Cameron Voloshin, Yisong Yue

POLITEX: Regret Bounds for Policy Iteration using Expert Prediction

Yasin Abbasi-Yadkori, Peter Bartlett, Kush Bhatia, Nevena Lazic, Csaba Szepesvári

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

Andrew Lawrence, Carl Henrik Ek, Neill Campbell

Lorentzian Distance Learning for Hyperbolic Representations

Marc Law, Renjie Liao, Jake Snell, Richard Zemel

A Better k-means++ Algorithm via Local Search

Silvio Lattanzi, Christian Sohler

Safe Policy Improvement with Baseline Bootstrapping

Romain Laroche, Paul Trichelair, Remi Tachet des Combes

Projection onto Minkowski Sums with Application to Constrained Learning

Joong-Ho Won, Jason Xu, Kenneth Lange

A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion

Sylvain Lamprier

State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations

Alex Lamb, Jonathan Binas, Anirudh Goyal, Sandeep Subramanian, Ioannis Mitliagkas

Characterizing Well-Behaved vs. Pathological Deep Neural Networks

Antoine Labatie

Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits

Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore

Making Decisions that Reduce Discriminatory Impacts

Matt Kusner, Chris Russell, Joshua Loftus, Ricardo Silva

A Large-Scale Study on Regularization and Normalization in GANs

Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

Geometry and Symmetry in Short-and-Sparse Deconvolution

Han-wen Kuo, Yenson Lau, Yuqian Zhang, John Wright

Loss Landscapes of Regularized Linear Autoencoders

Daniel Kunin, Jonathan Bloom, Aleksandrina Goeva, Cotton Seed

Faster Algorithms for Binary Matrix Factorization

Ravi Kumar, Rina Panigrahy, Ali Rahimi, David Woodruff

Estimate Sequences for Variance-Reduced Stochastic Composite Optimization

Andrei Kulunchakov, Julien Mairal

On the Complexity of Approximating Wasserstein Barycenters

Alexey Kroshnin, Nazarii Tupitsa, Darina Dvinskikh, Pavel Dvurechensky, Alexander Gasnikov

Simon Kornblith, Mohammad Norouzi, Honglak Lee, Geoffrey Hinton

LIT: Learned Intermediate Representation Training for Model Compression

Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia

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

Wouter Kool, Herke van Hoof, Max Welling

Robust Learning from Untrusted Sources

Nikola Konstantinov, Christoph Lampert

Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication

Anastasia Koloskova, Sebastian Stich, Martin Jaggi

POPQORN: Quantifying Robustness of Recurrent Neural Networks

Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong

Guarantees for Spectral Clustering with Fairness Constraints

Matthäus Kleindessner, Samira Samadi, Pranjal Awasthi, Jamie Morgenstern

Fair k-Center Clustering for Data Summarization

Matthäus Kleindessner, Pranjal Awasthi, Jamie Morgenstern

AUCμ: A Performance Metric for Multi-Class Machine Learning Models

Ross Kleiman, David Page

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces

Johannes Kirschner, Mojmir Mutny, Nicole Hiller, Rasmus Ischebeck, Andreas Krause

CompILE: Compositional Imitation Learning and Execution

Thomas Kipf, Yujia Li, Hanjun Dai, Vinicius Zambaldi, Alvaro Sanchez-Gonzalez

Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables

Friso Kingma, Pieter Abbeel, Jonathan Ho

Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension

Jisu KIM, Jaehyeok Shin, Alessandro Rinaldo, Larry Wasserman

Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model

Gi-Soo Kim, Myunghee Cho Paik

Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty

Youngjin Kim, Wontae Nam, Hyunwoo Kim, Ji-Hoon Kim, Gunhee Kim

FloWaveNet : A Generative Flow for Raw Audio

Sungwon Kim, Sang-gil Lee, Jongyoon Song, Jaehyeon Kim, Sungroh Yoon

EMI: Exploration with Mutual Information

Hyoungseok Kim, Jaekyeom Kim, Yeonwoo Jeong, Sergey Levine, Hyun Oh Song

Geometry Aware Convolutional Filters for Omnidirectional Images Representation

Renata Khasanova, Pascal Frossard

Collaborative Evolutionary Reinforcement Learning

Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer

CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network

Tom Kenter, Vincent Wan, Chun-An Chan, Rob Clark, Jakub Vit

Adaptive Scale-Invariant Online Algorithms for Learning Linear Models

Michal Kempka, Wojciech Kotlowski, Manfred K. Warmuth

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

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

Shallow-Deep Networks: Understanding and Mitigating Network Overthinking

Yigitcan Kaya, Sanghyun Hong, Tudor Dumitras

Robust Estimation of Tree Structured Gaussian Graphical Models

Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis

Processing Megapixel Images with Deep Attention-Sampling Models

Angelos Katharopoulos, Francois Fleuret

Neural Inverse Knitting: From Images to Manufacturing Instructions

Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Wojciech Matusik

Riemannian adaptive stochastic gradient algorithms on matrix manifolds

Hiroyuki Kasai, Pratik Jawanpuria, Bamdev Mishra

Error Feedback Fixes SignSGD and other Gradient Compression Schemes

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

Policy Consolidation for Continual Reinforcement Learning

Christos Kaplanis, Murray Shanahan, Claudia Clopath

Differentially Private Learning of Geometric Concepts

Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

Kirthevasan Kandasamy, Willie Neiswanger, Reed Zhang, Akshay Krishnamurthy, Jeff Schneider

Trainable Decoding of Sets of Sequences for Neural Sequence Models

Ashwin Kalyan, Peter Anderson, Stefan Lee, Dhruv Batra

Classifying Treatment Responders Under Causal Effect Monotonicity

Nathan Kallus

Robust Influence Maximization for Hyperparametric Models

Dimitris Kalimeris, Gal Kaplun, Yaron Singer

Molecular Hypergraph Grammar with Its Application to Molecular Optimization

Hiroshi Kajino

Statistical Foundations of Virtual Democracy

Anson Kahng, Min Kyung Lee, Ritesh Noothigattu, Ariel Procaccia, Christos-Alexandros Psomas

Bilinear Bandits with Low-rank Structure

Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak

GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver

David John, Vincent Heuveline, Michael Schober

Kernel Mean Matching for Content Addressability of GANs

Wittawat Jitkrittum, Patsorn Sangkloy, Muhammad Waleed Gondal, Amit Raj, James Hays

Discovering Options for Exploration by Minimizing Cover Time

Yuu Jinnai, Jee Won Park, David Abel, George Konidaris

Finding Options that Minimize Planning Time

Yuu Jinnai, David Abel, David Hershkowitz, Michael Littman, George Konidaris

Neural Logic Reinforcement Learning

zhengyao jiang, Shan Luo

Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization

Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang

Learning Discrete and Continuous Factors of Data via Alternating Disentanglement

Yeonwoo Jeong, Hyun Oh Song

Training CNNs with Selective Allocation of Channels

Jongheon Jeong, Jinwoo Shin

Ladder Capsule Network

Taewon Jeong, Youngmin Lee, Heeyoung Kim

Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance

Dasaem Jeong, Taegyun Kwon, Yoojin Kim, Juhan Nam

A Deep Reinforcement Learning Perspective on Internet Congestion Control

Nathan Jay, Noga Rotman, Brighten Godfrey, Michael Schapira, Aviv Tamar

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro Ortega

Learning What and Where to Transfer

Yunhun Jang, Hankook Lee, Sung Ju Hwang, Jinwoo Shin

DBSCAN++: Towards fast and scalable density clustering

Jennifer Jang, Heinrich Jiang

Sum-of-Squares Polynomial Flow

Priyank Jaini, Kira A. Selby, Yaoliang Yu

Differentially Private Fair Learning

Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea, Aaron Roth

Learning from a Learner

Alexis Jacq, Matthieu Geist, Ana Paiva, Olivier Pietquin

Causal Identification under Markov Equivalence: Completeness Results

Amin Jaber, Jiji Zhang, Elias Bareinboim

Complementary-Label Learning for Arbitrary Losses and Models

Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

Shariq Iqbal, Fei Sha

Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!

Niels Ipsen, Lars Kai Hansen

Learning Structured Decision Problems with Unawareness

Craig Innes, Alex Lascarides

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

HexaGAN: Generative Adversarial Nets for Real World Classification

Uiwon Hwang, Dahuin Jung, Sungroh Yoon

Composing Entropic Policies using Divergence Correction

Jonathan hunt, Andre Barreto, Timothy Lillicrap, Nicolas Heess

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models

Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment

Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Bautista Martin, Shih-Yu Sun

Stable and Fair Classification

Lingxiao Huang, Nisheeth Vishnoi

Hierarchical Importance Weighted Autoencoders

Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville

Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm

Kejun Huang, Xiao Fu

Unsupervised Deep Learning by Neighbourhood Discovery

Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu

Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization

Feihu Huang, Songcan Chen, Heng Huang

Bayesian Deconditional Kernel Mean Embeddings

Kelvin Hsu, Fabio Ramos

Classification from Positive, Unlabeled and Biased Negative Data

Yu-Guan Hsieh, Gang Niu, Masashi Sugiyama

Finding Mixed Nash Equilibria of Generative Adversarial Networks

Ya-Ping Hsieh, Chen Liu, Volkan Cevher

Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging

Ping-Chun Hsieh, Xi Liu, Anirban Bhattacharya, P R Kumar

Parameter-Efficient Transfer Learning for NLP

Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe

Nonconvex Variance Reduced Optimization with Arbitrary Sampling

Samuel Horváth, Peter Richtarik

Emerging Convolutions for Generative Normalizing Flows

Emiel Hoogeboom, Rianne van den Berg, Max Welling

Better generalization with less data using robust gradient descent

Matthew Holland, Kazushi Ikeda

Connectivity-Optimized Representation Learning via Persistent Homology

Christoph Hofer, Roland Kwitt, Marc Niethammer, Mandar Dixit

Collective Model Fusion for Multiple Black-Box Experts

Minh Hoang, Nghia Hoang, Bryan Kian Hsiang Low, Carleton Kingsford

Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules

Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, Pieter Abbeel

Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel

Using Pre-Training Can Improve Model Robustness and Uncertainty

Dan Hendrycks, Kimin Lee, Mantas Mazeika