Papers
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
Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj et al.
$D^2$: Decentralized Training over Decentralized Data
Hanlin Tang, Xiangru Lian, Ming Yan et al.
A Boo(n) for Evaluating Architecture Performance
Ondrej Bajgar, Rudolf Kadlec, Jan Kleindienst
Accelerated Spectral Ranking
Arpit Agarwal, Prathamesh Patil, Shivani Agarwal
Accelerating Greedy Coordinate Descent Methods
Haihao Lu, Robert Freund, Vahab Mirrokni
Accelerating Natural Gradient with Higher-Order Invariance
Yang Song, Jiaming Song, Stefano Ermon
Accurate Inference for Adaptive Linear Models
Yash Deshpande, Lester Mackey, Vasilis Syrgkanis et al.
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon
A Classification-Based Study of Covariate Shift in GAN Distributions
Shibani Santurkar, Ludwig Schmidt, Aleksander Madry
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Alp Yurtsever, Olivier Fercoq, Francesco Locatello et al.