Locally Private Nonparametric Contextual Multi-armed Bandits
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912292236427264 |
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| author | Ma, Yuheng Jiang, Feiyu Zhao, Zifeng Yang, Hanfang Yu, Yi |
| author_facet | Ma, Yuheng Jiang, Feiyu Zhao, Zifeng Yang, Hanfang Yu, Yi |
| contents | Motivated by privacy concerns in sequential decision-making on sensitive data, we address the challenge of nonparametric contextual multi-armed bandits (MAB) under local differential privacy (LDP). We develop a uniform-confidence-bound-type estimator, showing its minimax optimality supported by a matching minimax lower bound. We further consider the case where auxiliary datasets are available, subject also to (possibly heterogeneous) LDP constraints. Under the widely-used covariate shift framework, we propose a jump-start scheme to effectively utilize the auxiliary data, the minimax optimality of which is further established by a matching lower bound. Comprehensive experiments on both synthetic and real-world datasets validate our theoretical results and underscore the effectiveness of the proposed methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08098 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Locally Private Nonparametric Contextual Multi-armed Bandits Ma, Yuheng Jiang, Feiyu Zhao, Zifeng Yang, Hanfang Yu, Yi Machine Learning Methodology Motivated by privacy concerns in sequential decision-making on sensitive data, we address the challenge of nonparametric contextual multi-armed bandits (MAB) under local differential privacy (LDP). We develop a uniform-confidence-bound-type estimator, showing its minimax optimality supported by a matching minimax lower bound. We further consider the case where auxiliary datasets are available, subject also to (possibly heterogeneous) LDP constraints. Under the widely-used covariate shift framework, we propose a jump-start scheme to effectively utilize the auxiliary data, the minimax optimality of which is further established by a matching lower bound. Comprehensive experiments on both synthetic and real-world datasets validate our theoretical results and underscore the effectiveness of the proposed methods. |
| title | Locally Private Nonparametric Contextual Multi-armed Bandits |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2503.08098 |