Papers
Conditional Independence in Testing Bayesian Networks
Yujia Shen, Haiying Huang, Arthur Choi et al.
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
Vatsal Sharan, Kai Sheng Tai, Peter Bailis et al.
Exploration Conscious Reinforcement Learning Revisited
Lior Shani, Yonathan Efroni, Shie Mannor
On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference
Rohin Shah, Noah Gundotra, Pieter Abbeel et al.
Discovering Context Effects from Raw Choice Data
Arjun Seshadri, Alex Peysakhovich, Johan Ugander
Weakly-Supervised Temporal Localization via Occurrence Count Learning
Julien Schroeter, Kirill Sidorov, David Marshall
Locally Private Bayesian Inference for Count Models
Aaron Schein, Zhiwei Steven Wu, Alexandra Schofield et al.
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Nikunj Saunshi, Orestis Plevrakis, Sanjeev Arora et al.
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
Ikuro Sato, Kohta Ishikawa, Guoqing Liu et al.
Near optimal finite time identification of arbitrary linear dynamical systems
Tuhin Sarkar, Alexander Rakhlin
Multivariate Submodular Optimization
Richard Santiago, F. Bruce Shepherd
Deep Gaussian Processes with Importance-Weighted Variational Inference
Hugh Salimbeni, Vincent Dutordoir, James Hensman et al.
An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule
Touqir Sajed, Or Sheffet
White-box vs Black-box: Bayes Optimal Strategies for Membership Inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid et al.
Plug-and-Play Methods Provably Converge with Properly Trained Denoisers
Ernest Ryu, Jialin Liu, Sicheng Wang et al.
A Contrastive Divergence for Combining Variational Inference and MCMC
Francisco Ruiz, Michalis Titsias
Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar et al.
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy et al.
Neuron birth-death dynamics accelerates gradient descent and converges asymptotically
Grant Rotskoff, Samy Jelassi, Joan Bruna et al.
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth, Yannic Kilcher, Thomas Hofmann
Good Initializations of Variational Bayes for Deep Models
Simone Rossi, Pietro Michiardi, Maurizio Filippone
Online Convex Optimization in Adversarial Markov Decision Processes
Aviv Rosenberg, Yishay Mansour
Separating value functions across time-scales
Joshua Romoff, Peter Henderson, Ahmed Touati et al.
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
Paul Rolland, Ali Kavis, Alexander Immer et al.
A Persistent Weisfeiler-Lehman Procedure for Graph Classification
Bastian Rieck, Christian Bock, Karsten Borgwardt