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John Lui

2 accepted papers

2022

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

NeurIPS 2022accept

In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size $K$ in the regret bound, where $K$ is the total number of arms that can be pulled or triggered in each round. First, for the setting of CMAB with probabilistically triggered arms (CMA…

Cited by 23SourcePDFScholar
2021

Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback

NeurIPS 2021poster

This paper studies a cooperative multi-armed bandit problem with $M$ agents cooperating together to solve the same instance of a $K$-armed stochastic bandit problem with the goal of maximizing the cumulative reward of agents. The agents are heterogeneous in (i) their limited access to a local subset…

Cited by 32SourcePDFScholar