4 papers
Unlearning Offline Stochastic Multi-Armed Bandits
Zichun Ye, Runqi Wang, Xuchuang Wang +3
Machine unlearning aims to unlearn data points from a learned model, offering a principled way to process data-deletion requests and mitigate privacy risks without full retraining.…
Near-Optimal Regret for Efficient Stochastic Combinatorial Semi-Bandits
Zichun Ye, Runqi Wang, Xutong Liu +1
The combinatorial multi-armed bandit (CMAB) is a cornerstone of sequential decision-making framework, dominated by two algorithmic families: UCB-based and adversarial methods such…
Tight Gap-Dependent Memory-Regret Trade-Off for Single-Pass Streaming Stochastic Multi-Armed Bandits
Zichun Ye, Chihao Zhang, Jiahao Zhao
We study the problem of minimizing gap-dependent regret for single-pass streaming stochastic multi-armed bandits (MAB). In this problem, the arms are present in a stream, and a…
Understanding Memory-Regret Trade-Off for Streaming Stochastic Multi-Armed Bandits
Yuchen He, Zichun Ye, Chihao Zhang
We study the stochastic multi-armed bandit problem in the -pass streaming model. In this problem, the arms are present in a stream and at most arms and their statistic…