Search Results for author: Po-An Wang

Found 6 papers, 1 papers with code

Best Arm Identification with Fixed Budget: A Large Deviation Perspective

1 code implementation NeurIPS 2023 Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere

In particular, we present \sred (Continuous Rejects), a truly adaptive algorithm that can reject arms in {\it any} round based on the observed empirical gaps between the rewards of various arms.

Multi-Armed Bandits

On Uniformly Optimal Algorithms for Best Arm Identification in Two-Armed Bandits with Fixed Budget

no code implementations23 Aug 2023 Po-An Wang, Kaito Ariu, Alexandre Proutiere

We prove that there is no algorithm that (i) performs as well as the algorithm sampling each arm equally (this algorithm is referred to as the {\it uniform sampling} algorithm) on all instances, and that (ii) strictly outperforms this algorithm on at least one instance.

Improved analysis of randomized SVD for top-eigenvector approximation

no code implementations16 Feb 2022 Ruo-Chun Tzeng, Po-An Wang, Florian Adriaens, Aristides Gionis, Chi-Jen Lu

We present a novel analysis of the randomized SVD algorithm of \citet{halko2011finding} and derive tight bounds in many cases of interest.

Fast Pure Exploration via Frank-Wolfe

no code implementations NeurIPS 2021 Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere

For this problem, instance-specific lower bounds on the expected sample complexity reveal the optimal proportions of arm draws an Oracle algorithm would apply.

An Optimal Algorithm for Multiplayer Multi-Armed Bandits

no code implementations28 Sep 2019 Alexandre Proutiere, Po-An Wang

We present DPE (Decentralized Parsimonious Exploration), a decentralized algorithm that achieves the same regret as that obtained by an optimal centralized algorithm.

Multi-Armed Bandits

Tensor Decomposition via Simultaneous Power Iteration

no code implementations ICML 2017 Po-An Wang, Chi-Jen Lu

Tensor decomposition is an important problem with many applications across several disciplines, and a popular approach for this problem is the tensor power method.

Tensor Decomposition

Cannot find the paper you are looking for? You can Submit a new open access paper.