Papers
Generalized Linear Rule Models
Dennis Wei, Sanjeeb Dash, Tian Gao et al.
AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes
Rachel Ward, Xiaoxia Wu, Leon Bottou
Jumpout : Improved Dropout for Deep Neural Networks with ReLUs
Shengjie Wang, Tianyi Zhou, Jeff Bilmes
Bias Also Matters: Bias Attribution for Deep Neural Network Explanation
Shengjie Wang, Tianyi Zhou, Jeff Bilmes
On the Generalization Gap in Reparameterizable Reinforcement Learning
Huan Wang, Stephan Zheng, Caiming Xiong et al.
Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random
Xiaojie Wang, Rui Zhang, Yu Sun et al.
On Sparse Linear Regression in the Local Differential Privacy Model
Di Wang, Jinhui Xu
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang, Berk Ustun, Flavio Calmon
Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle Maddix et al.
State-Regularized Recurrent Neural Networks
Cheng Wang, Mathias Niepert
On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey et al.
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models
Dilin Wang, Qiang Liu
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler et al.
Improving Neural Language Modeling via Adversarial Training
Dilin Wang, Chengyue Gong, Qiang Liu
SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya Donti, Bryan Wilder et al.
Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
Ruohan Wang, Carlo Ciliberto, Pierluigi Vito Amadori et al.
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
Di Wang, Changyou Chen, Jinhui Xu
Convolutional Poisson Gamma Belief Network
Chaojie Wang, Bo Chen, Sucheng Xiao et al.
Graph Convolutional Gaussian Processes
Ian Walker, Ben Glocker
On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian Fuchs, Martin Engelcke et al.
Learning to select for a predefined ranking
Aleksei Ustimenko, Aleksandr Vorobev, Gleb Gusev et al.
On the Design of Estimators for Bandit Off-Policy Evaluation
Nikos Vlassis, Aurelien Bibaut, Maria Dimakopoulou et al.
Understanding Priors in Bayesian Neural Networks at the Unit Level
Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo et al.
Maximum Likelihood Estimation for Learning Populations of Parameters
Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant et al.