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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.20734 |
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| _version_ | 1866912803461267456 |
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| author | Cheng, Zhuoyu Hatano, Kohei Takimoto, Eiji |
| author_facet | Cheng, Zhuoyu Hatano, Kohei Takimoto, Eiji |
| contents | We consider a bandit optimization problem for nonconvex and non-smooth functions, where in each trial the loss function is the sum of a linear function and a small but arbitrary perturbation chosen after observing the player's choice. We give both expected and high probability regret bounds for the problem. Our result also implies an improved high-probability regret bound for the bandit linear optimization, a special case with no perturbation. We also give a lower bound on the expected regret. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20734 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adversarial bandit optimization for approximately linear functions Cheng, Zhuoyu Hatano, Kohei Takimoto, Eiji Machine Learning Artificial Intelligence We consider a bandit optimization problem for nonconvex and non-smooth functions, where in each trial the loss function is the sum of a linear function and a small but arbitrary perturbation chosen after observing the player's choice. We give both expected and high probability regret bounds for the problem. Our result also implies an improved high-probability regret bound for the bandit linear optimization, a special case with no perturbation. We also give a lower bound on the expected regret. |
| title | Adversarial bandit optimization for approximately linear functions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.20734 |