4 citations · 5 across the 5 of their papers we have counts for
6 papers
Thompson Sampling for Learning in Zero-Sum Games
Kenshi Abe, Kaito Ariu, Po-An Wang
Thompson sampling (TS) achieves logarithmic and asymptotically optimal regret in classical stochastic bandits, but its behavior in multi-agent learning remains less understood. We…
Policy Testing in Markov Decision Processes
Kaito Ariu, Po-An Wang, Alexandre Proutiere +1
We study the policy testing problem in discounted Markov decision processes (MDPs) in the fixed-confidence setting under a generative model with static sampling. The goal is to dec…
Best Arm Identification with Fixed Budget: A Large Deviation Perspective
Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere
We consider the problem of identifying the best arm in stochastic Multi-Armed Bandits (MABs) using a fixed sampling budget. Characterizing the minimal instance-specific error proba…
On Universally Optimal Algorithms for A/B Testing
Po-An Wang, Kaito Ariu, Alexandre Proutiere
We study the problem of best-arm identification with fixed budget in stochastic multi-armed bandits with Bernoulli rewards. For the problem with two arms, also known as the A/B tes…
Improved analysis of randomized SVD for top-eigenvector approximation
Ruo-Chun Tzeng, Po-An Wang, Florian Adriaens +2
Computing the top eigenvectors of a matrix is a problem of fundamental interest to various fields. While the majority of the literature has focused on analyzing the reconstruction…
An Optimal Algorithm for Multiplayer Multi-Armed Bandits
Alexandre Proutiere, Po-An Wang
The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where decision makers or players collaborate to maximize their cumulative reward. When several players se…