5 papers
Robustness to Model Approximation, Model Learning From Data, and Sample Complexity in Wasserstein Regular MDPs
Yichen Zhou, Yanglei Song, Serdar Yüksel
The paper studies the robustness properties of discrete-time stochastic optimal control under Wasserstein model approximation for both discounted-cost and average-cost criteria. Sp…
Sequential Multiple Testing: A Second-Order Asymptotic Analysis
Jingyu Liu, Yanglei Song
We study sequential multiple testing with independent data streams, where the goal is to identify an unknown subset of signals while controlling commonly used error metrics, includ…
Efficient Importance Sampling for Wrong Exit Probabilities over Combinatorially Many Rare Regions
Yanglei Song, Georgios Fellouris
We consider importance sampling for estimating the probability that a light-tailed -dimensional random walk exits through one of many disjoint rare-event regions before reaching…
Optimal Decision Rules for Composite Binary Hypothesis Testing under Neyman-Pearson Framework
Yanglei Song, Berkan Dulek, Sinan Gezici
The composite binary hypothesis testing problem within the Neyman-Pearson framework is considered. The goal is to maximize the expectation of a nonlinear function of the detection…
Minimax Rate-Optimal Algorithms for High-Dimensional Stochastic Linear Bandits
Jingyu Liu, Yanglei Song
We study the stochastic linear bandit problem with multiple arms over rounds, where the covariate dimension may exceed , but each arm-specific parameter vector is -sp…