Papers
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
Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan, J. Zico Kolter
Efficient Algorithms for Smooth Minimax Optimization
Kiran K Thekumparampil, Prateek Jain, Praneeth Netrapalli et al.
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite et al.
Capacity Bounded Differential Privacy
Kamalika Chaudhuri, Jacob Imola, Ashwin Machanavajjhala
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Igor Fedorov, Ryan P. Adams, Matthew Mattina et al.
Random Tessellation Forests
Shufei Ge, Shijia Wang, Yee Whye Teh et al.
Spherical Text Embedding
Yu Meng, Jiaxin Huang, Guangyuan Wang et al.