Papers
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension
Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo et al.
Unifying Orthogonal Monte Carlo Methods
Krzysztof Choromanski, Mark Rowland, Wenyu Chen et al.
Universal Multi-Party Poisoning Attacks
Saeed Mahloujifar, Mohammad Mahmoody, Ameer Mohammed
Unreproducible Research is Reproducible
Xavier Bouthillier, César Laurent, Pascal Vincent
Unsupervised Deep Learning by Neighbourhood Discovery
Jiabo Huang, Qi Dong, Shaogang Gong et al.
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo, Diego Ortego, Paul Albert et al.
Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks, Kimin Lee, Mantas Mazeika
Validating Causal Inference Models via Influence Functions
Ahmed Alaa, Mihaela Van Der Schaar
Variational Annealing of GANs: A Langevin Perspective
Chenyang Tao, Shuyang Dai, Liqun Chen et al.
Variational Implicit Processes
Chao Ma, Yingzhen Li, Jose Miguel Hernandez-Lobato
Variational Inference for sparse network reconstruction from count data
Julien Chiquet, Stephane Robin, Mahendra Mariadassou
Variational Laplace Autoencoders
Yookoon Park, Chris Kim, Gunhee Kim
Variational Russian Roulette for Deep Bayesian Nonparametrics
Kai Xu, Akash Srivastava, Charles Sutton
Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration
Vladislav Polianskii, Florian T. Pokorny
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé Iii et al.
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank Schmidt, Zico Kolter
Wasserstein of Wasserstein Loss for Learning Generative Models
Yonatan Dukler, Wuchen Li, Alex Lin et al.
Weak Detection of Signal in the Spiked Wigner Model
Hye Won Chung, Ji Oon Lee
Weakly-Supervised Temporal Localization via Occurrence Count Learning
Julien Schroeter, Kirill Sidorov, David Marshall
What is the Effect of Importance Weighting in Deep Learning?
Jonathon Byrd, Zachary Lipton
When Samples Are Strategically Selected
Hanrui Zhang, Yu Cheng, Vincent Conitzer
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid et al.
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem
Alon Brutzkus, Amir Globerson
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
Cong Xie, Sanmi Koyejo, Indranil Gupta