Learning Graphical Models
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples Hao Sun
We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high
Near-OptimalReinforcementLearningwithSelf-Play
This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn theoptimal policy by playing againstitself without any direct supervision.
SparseBayesianGenerativeModeling forCompressiveSensing
It then serves as probabilistic prior to regularize the inverse problem(1).
Estimating the Rate-Distortion Function by Wasserstein Gradient Descent Yibo Yang 1 Stephan Eckstein 2 Marcel Nutz 3 Stephan Mandt 1 1
In this work, we propose a new approach to R-D estimation from the perspective of optimal transport.
Exact Bayesian Inference on Discrete Models via Probability Generating Functions: A Probabilistic Programming Approach
Our experiments show that Genfer is often faster than the existing exact inference tools PSI, Dice, and Prodigy.