Unlearning Offline Stochastic Multi-Armed Bandits
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. Prior work has mainly studied unsupervised / supervised machine unlearning, leaving unlearning for sequential decision-making systems far less understood. We initiate the first study of a foundational sequential decision-making problem: offline stochastic multi-armed bandits (MAB). We formalize the privacy constraint for offline MAB and measure utility by the post-unlearning decision quality. We conduct a systematic study of both single- and multi-source unlearning scenarios under two data-generation models, th...
| Authors | Zichun Ye, Runqi Wang, Xuchuang Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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