Papers
Power analysis of knockoff filters for correlated designs
Jingbo Liu, Philippe Rigollet
Linear Stochastic Bandits Under Safety Constraints
Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node
Suhas Jayaram Subramanya, Fnu Devvrit, Harsha Vardhan Simhadri et al.
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna et al.
Coda: An End-to-End Neural Program Decompiler
Cheng Fu, Huili Chen, Haolan Liu et al.
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
DongDong Ge, Haoyue Wang, Zikai Xiong et al.
Concentration of risk measures: A Wasserstein distance approach
Sanjay P. Bhat, Prashanth L.A.
Semi-Parametric Efficient Policy Learning with Continuous Actions
Victor Chernozhukov, Mert Demirer, Greg Lewis et al.
Equal Opportunity in Online Classification with Partial Feedback
Yahav Bechavod, Katrina Ligett, Aaron Roth et al.
The Geometry of Deep Networks: Power Diagram Subdivision
Randall Balestriero, Romain Cosentino, Behnaam Aazhang et al.
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits
Wenhao Zhang, Si Wu, Brent Doiron et al.
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning
Yali Du, Lei Han, Meng Fang et al.
Universal Boosting Variational Inference
Trevor Campbell, Xinglong Li
Differentially Private Markov Chain Monte Carlo
Mikko Heikkilä, Joonas Jälkö, Onur Dikmen et al.
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade et al.
Reliable training and estimation of variance networks
Nicki Skafte, Martin Jørgensen, Søren Hauberg
State Aggregation Learning from Markov Transition Data
Yaqi Duan, Tracy Ke, Mengdi Wang
Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor
Meera Pai, Animesh Kumar
Bayesian Optimization under Heavy-tailed Payoffs
Sayak Ray Chowdhury, Aditya Gopalan
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals
Surbhi Goel, Sushrut Karmalkar, Adam Klivans
Meta-Surrogate Benchmarking for Hyperparameter Optimization
Aaron Klein, Zhenwen Dai, Frank Hutter et al.
Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections
Raanan Yehezkel Rohekar, Yaniv Gurwicz, Shami Nisimov et al.
Robust exploration in linear quadratic reinforcement learning
Jack Umenberger, Mina Ferizbegovic, Thomas B Schön et al.
First order expansion of convex regularized estimators
Pierre Bellec, Arun Kuchibhotla