Papers
Modern Neural Networks Generalize on Small Data Sets
Matthew Olson, Abraham Wyner, Richard Berk
Equality of Opportunity in Classification: A Causal Approach
Junzhe Zhang, Elias Bareinboim
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
Tianyi Chen, Georgios Giannakis, Tao Sun et al.
Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
Abhinav Gupta, Adithyavairavan Murali, Dhiraj Prakashchand Gandhi et al.
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
Yaodong Yu, Pan Xu, Quanquan Gu
Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
Simon S Du, Wei Hu, Jason Lee
Dual Swap Disentangling
Zunlei Feng, Xinchao Wang, Chenglong Ke et al.
A Stein variational Newton method
Gianluca Detommaso, Tiangang Cui, Youssef Marzouk et al.
Flexible neural representation for physics prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang et al.
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo, Jianhong Wang, Yaodong Yang et al.
Practical exact algorithm for trembling-hand equilibrium refinements in games
Gabriele Farina, Nicola Gatti, Tuomas Sandholm
Non-Local Recurrent Network for Image Restoration
Ding Liu, Bihan Wen, Yuchen Fan et al.
Sharp Bounds for Generalized Uniformity Testing
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
SEGA: Variance Reduction via Gradient Sketching
Filip Hanzely, Konstantin Mishchenko, Peter Richtarik
Loss Functions for Multiset Prediction
Sean Welleck, Zixin Yao, Yu Gai et al.
A Mathematical Model For Optimal Decisions In A Representative Democracy
Malik Magdon-Ismail, Lirong Xia
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators
Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda et al.
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Tianyi Liu, Shiyang Li, Jianping Shi et al.
Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives
Song Zhou, Swati Gupta, Madeleine Udell
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
Richard Zhang, Cedric Josz, Somayeh Sojoudi et al.
A Practical Algorithm for Distributed Clustering and Outlier Detection
Jiecao Chen, Erfan Sadeqi Azer, Qin Zhang
Fast deep reinforcement learning using online adjustments from the past
Steven Hansen, Alexander Pritzel, Pablo Sprechmann et al.
Improved Network Robustness with Adversary Critic
Alexander Matyasko, Lap-Pui Chau
Assessing Generative Models via Precision and Recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic et al.
Adversarially Robust Generalization Requires More Data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras et al.