ICML 2020

1,084 papers

Consistent Structured Prediction with Max-Min Margin Markov Networks

Alex Nowak, Francis Bach, Alessandro Rudi

Supervised learning: no loss no cry

Richard Nock, Aditya Menon

Semi-Supervised StyleGAN for Disentanglement Learning

Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney

LP-SparseMAP: Differentiable Relaxed Optimization for Sparse Structured Prediction

Vlad Niculae, Andre Martins

Streaming k-Submodular Maximization under Noise subject to Size Constraint

Lan Nguyen, My T. Thai

Robust Bayesian Classification Using An Optimistic Score Ratio

Viet Anh Nguyen, Nian Si, Jose Blanchet

Knowing The What But Not The Where in Bayesian Optimization

Vu Nguyen, Michael A. Osborne

Graph Homomorphism Convolution

Hoang Nguyen, Takanori Maehara

LEEP: A New Measure to Evaluate Transferability of Learned Representations

Cuong Nguyen, Tal Hassner, Matthias Seeger, Cédric Archambeau

Aggregation of Multiple Knockoffs

Tuan-Binh Nguyen, Jerome-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot

Involutive MCMC: a Unifying Framework

Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov

In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors

Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel Roy

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Geoffrey Negiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello

Oracle Efficient Private Non-Convex Optimization

Seth Neel, Aaron Roth, Giuseppe Vietri, Steven Wu

Bayesian Sparsification of Deep C-valued Networks

Ivan Nazarov, Evgeny Burnaev

PolyGen: An Autoregressive Generative Model of 3D Meshes

Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, Peter Battaglia

Goal-Aware Prediction: Learning to Model What Matters

Suraj Nair, Silvio Savarese, Chelsea Finn

Up or Down? Adaptive Rounding for Post-Training Quantization

Markus Nagel, Rana Ali Amjad, Mart van Baalen, Christos Louizos, Tijmen Blankevoort

From Chaos to Order: Symmetry and Conservation Laws in Game Dynamics

Sai Ganesh Nagarajan, David Balduzzi, Georgios Piliouras

Reliable Fidelity and Diversity Metrics for Generative Models

Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, Jaejun Yoo

Voice Separation with an Unknown Number of Multiple Speakers

Eliya Nachmani, Yossi Adi, Lior Wolf

Full Law Identification in Graphical Models of Missing Data: Completeness Results

Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser

Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees

Sen Na, Yuwei Luo, Zhuoran Yang, Zhaoran Wang, Mladen Kolar

Missing Data Imputation using Optimal Transport

Boris Muzellec, Julie Josse, Claire Boyer, Marco Cuturi

Fast computation of Nash Equilibria in Imperfect Information Games

Rémi Munos, Julien Pérolat, Jean-Baptiste Lespiau, Mark Rowland, Bart De Vylder

Unique Properties of Flat Minima in Deep Networks

Rotem Mulayoff, Tomer Michaeli

Two Simple Ways to Learn Individual Fairness Metrics from Data

Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun

Continuous-time Lower Bounds for Gradient-based Algorithms

Michael Muehlebach, Michael Jordan

Consistent Estimators for Learning to Defer to an Expert

Hussein Mozannar, David Sontag

Fair Learning with Private Demographic Data

Hussein Mozannar, Mesrob Ohannessian, Nathan Srebro

Explainable k-Means and k-Medians Clustering

Michal Moshkovitz, Sanjoy Dasgupta, Cyrus Rashtchian, Nave Frost

Topological Autoencoders

Michael Moor, Max Horn, Bastian Rieck, Karsten Borgwardt

Confidence-Aware Learning for Deep Neural Networks

Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang

An end-to-end approach for the verification problem: learning the right distance

Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R Devon Hjelm, Tiago Falk

Efficiently Learning Adversarially Robust Halfspaces with Noise

Omar Montasser, Surbhi Goel, Ilias Diakonikolas, Nathan Srebro

Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time

Zahra Monfared, Daniel Durstewitz

Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan

Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning

Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford

Coresets for Data-efficient Training of Machine Learning Models

Baharan Mirzasoleiman, Jeff Bilmes, Jure Leskovec

Learning Reasoning Strategies in End-to-End Differentiable Proving

Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel

Automatic Shortcut Removal for Self-Supervised Representation Learning

Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen

Strategic Classification is Causal Modeling in Disguise

John Miller, Smitha Milli, Moritz Hardt

The Effect of Natural Distribution Shift on Question Answering Models

John Miller, Karl Krauth, Benjamin Recht, Ludwig Schmidt

VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing

Zoltán Milacski, Barnabás Póczos, Andras Lorincz

Projective Preferential Bayesian Optimization

Petrus Mikkola, Milica Todorović, Jari Järvi, Patrick Rinke, Samuel Kaski

The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture

Francesca Mignacco, Florent Krzakala, Yue Lu, Pierfrancesco Urbani, Lenka Zdeborová

Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning

Alberto Maria Metelli, Flavio Mazzolini, Lorenzo Bisi, Luca Sabbioni, Marcello Restelli

Training Binary Neural Networks using the Bayesian Learning Rule

Xiangming Meng, Roman Bachmann, Mohammad Emtiyaz Khan

Randomized Block-Diagonal Preconditioning for Parallel Learning

Celestine Mendler-Dünner, Aurelien Lucchi

Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM

Kunal Menda, Jean De Becdelievre, Jayesh Gupta, Ilan Kroo, Mykel Kochenderfer

On the Global Convergence Rates of Softmax Policy Gradient Methods

Jincheng Mei, Chenjun Xiao, Csaba Szepesvári, Dale Schuurmans

Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification

Hongyuan Mei, Guanghui Qin, Minjie Xu, Jason Eisner

On Approximate Thompson Sampling with Langevin Algorithms

Eric Mazumdar, Aldo Pacchiano, Yian Ma, Michael Jordan, Peter Bartlett

Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations

Robert Mattila, Cristian Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg

Adding seemingly uninformative labels helps in low data regimes

Christos Matsoukas, Albert Bou Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda

Predictive Multiplicity in Classification

Charles Marx, Flavio Calmon, Berk Ustun

Minimax Pareto Fairness: A Multi Objective Perspective

Natalia Martinez, Martin Bertran, Guillermo Sapiro

Stochastically Dominant Distributional Reinforcement Learning

John Martin, Michal Lyskawinski, Xiaohu Li, Brendan Englot

On Learning Sets of Symmetric Elements

Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

Adaptive Adversarial Multi-task Representation Learning

Yuren Mao, Weiwei Liu, Xuemin Lin

Emergence of Separable Manifolds in Deep Language Representations

Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang

Adaptive Gradient Descent without Descent

Yura Malitsky, Konstantin Mishchenko

From Local SGD to Local Fixed-Point Methods for Federated Learning

Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, Peter Richtarik

Proving the Lottery Ticket Hypothesis: Pruning is All You Need

Eran Malach, Gilad Yehudai, Shai Shalev-Schwartz, Ohad Shamir

Optimal transport mapping via input convex neural networks

Ashok Makkuva, Amirhossein Taghvaei, Sewoong Oh, Jason Lee

Estimation of Bounds on Potential Outcomes For Decision Making

Maggie Makar, Fredrik Johansson, John Guttag, David Sontag

Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination

Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer

Adversarial Robustness Against the Union of Multiple Perturbation Models

Pratyush Maini, Eric Wong, Zico Kolter

Convergence of a Stochastic Gradient Method with Momentum for Non-Smooth Non-Convex Optimization

Vien Mai, Mikael Johansson

Anderson Acceleration of Proximal Gradient Methods

Vien Mai, Mikael Johansson

How recurrent networks implement contextual processing in sentiment analysis

Niru Maheswaranathan, David Sussillo

Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization

Debabrata Mahapatra, Vaibhav Rajan

Individual Fairness for k-Clustering

Sepideh Mahabadi, Ali Vakilian

Adversarial Neural Pruning with Latent Vulnerability Suppression

Divyam Madaan, Jinwoo Shin, Sung Ju Hwang

Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle

Shaocong Ma, Yi Zhou

Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints

Runchao Ma, Qihang Lin, Tianbao Yang

Normalized Loss Functions for Deep Learning with Noisy Labels

Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani

Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space

Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu, Xinhua Zhang

Efficient Continuous Pareto Exploration in Multi-Task Learning

Pingchuan Ma, Tao Du, Wojciech Matusik

Bandits with Adversarial Scaling

Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme

Progressive Identification of True Labels for Partial-Label Learning

Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng

Learning Algebraic Multigrid Using Graph Neural Networks

Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, Irad Yavneh

Adversarial Nonnegative Matrix Factorization

Lei Luo, Yanfu Zhang, Heng Huang

Progressive Graph Learning for Open-Set Domain Adaptation

Yadan Luo, Zijian Wang, Zi Huang, Mahsa Baktashmotlagh

Improved Communication Cost in Distributed PageRank Computation – A Theoretical Study

Siqiang Luo

Does label smoothing mitigate label noise?

MICHAL LUKASIK, Srinadh Bhojanapalli, Aditya Menon, Sanjiv Kumar

Countering Language Drift with Seeded Iterated Learning

Yuchen Lu, Soumye Singhal, Florian Strub, Aaron Courville, Olivier Pietquin

A Mean Field Analysis Of Deep ResNet And Beyond: Towards Provably Optimization Via Overparameterization From Depth

Yiping Lu, Chao Ma, Yulong Lu, Jianfeng Lu, Lexing Ying

Moniqua: Modulo Quantized Communication in Decentralized SGD

Yucheng Lu, Christopher De Sa

Working Memory Graphs

Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht

Differentiating through the Fréchet Mean

Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim

Error Estimation for Sketched SVD via the Bootstrap

Miles Lopes, N. Benjamin Erichson, Michael Mahoney

Stochastic Hamiltonian Gradient Methods for Smooth Games

Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien

Too Relaxed to Be Fair

Michael Lohaus, Michaël Perrot, Ulrike Von Luxburg

Weakly-Supervised Disentanglement Without Compromises

Francesco Locatello, Ben Poole, Gunnar Raetsch, Bernhard Schölkopf, Olivier Bachem

Finding trainable sparse networks through Neural Tangent Transfer

Tianlin Liu, Friedemann Zenke

Learning to Encode Position for Transformer with Continuous Dynamical Model

Xuanqing Liu, Hsiang-Fu Yu, Inderjit Dhillon, Cho-jui Hsieh

Learning Deep Kernels for Non-Parametric Two-Sample Tests

Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton

A Generic First-Order Algorithmic Framework for Bi-Level Programming Beyond Lower-Level Singleton

Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, Jin Zhang