Zheshun Wu

2 accepted papers

2026

Bandit Learning in Matching Markets Robust to Adversarial Corruptions

Zheshun Wu, Jinhang Zuo, Zenglin Xu, Fang Kong

ICLR 2026poster

This paper investigates the problem of bandit learning in two-sided decentralized matching markets with adversarial corruptions. In matching markets, players on one side aim to learn their unknown preferences over arms on the other side through iterative online learning, with the goal of identifying…

Cited by 0 Source ↗Scholar ↗

2024

On the Necessity of Collaboration for Online Model Selection with Decentralized Data

Junfan Li, Zheshun Wu, Zenglin Xu, Irwin King

NeurIPS 2024poster

We consider online model selection with decentralized data over $M$ clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity, while we answer the question from a novel perspective of computational const…

Cited by 3 Source ↗PDF ↗Scholar ↗