DAL: A Practical Prior-Free Black-Box Framework for Non-Stationary Bandits
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912614027624448 |
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| author | Gerogiannis, Argyrios Huang, Yu-Han Bose, Subhonmesh Veeravalli, Venugopal V. |
| author_facet | Gerogiannis, Argyrios Huang, Yu-Han Bose, Subhonmesh Veeravalli, Venugopal V. |
| contents | We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of non-stationary bandits without prior knowledge of the underlying non-stationarity. DAL accepts any stationary bandit algorithm as input and augments it with a change detector, enabling applicability to all common bandit variants. Extensive experimentation demonstrates that DAL consistently surpasses current state-of-the-art methods across diverse non-stationary scenarios, including synthetic benchmarks and real-world datasets, underscoring its versatility and scalability. We provide theoretical insights into DAL's strong empirical performance, complemented by thorough experimental validation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19401 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DAL: A Practical Prior-Free Black-Box Framework for Non-Stationary Bandits Gerogiannis, Argyrios Huang, Yu-Han Bose, Subhonmesh Veeravalli, Venugopal V. Machine Learning We introduce a practical, black-box framework termed Detection Augmented Learning (DAL) for the problem of non-stationary bandits without prior knowledge of the underlying non-stationarity. DAL accepts any stationary bandit algorithm as input and augments it with a change detector, enabling applicability to all common bandit variants. Extensive experimentation demonstrates that DAL consistently surpasses current state-of-the-art methods across diverse non-stationary scenarios, including synthetic benchmarks and real-world datasets, underscoring its versatility and scalability. We provide theoretical insights into DAL's strong empirical performance, complemented by thorough experimental validation. |
| title | DAL: A Practical Prior-Free Black-Box Framework for Non-Stationary Bandits |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2501.19401 |