Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929500109930496 |
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| author | Swiers, Rowan Prabanantham, Subash Maher, Andrew |
| author_facet | Swiers, Rowan Prabanantham, Subash Maher, Andrew |
| contents | Multi-armed Bandits (MABs) are increasingly employed in online platforms and e-commerce to optimize decision making for personalized user experiences. In this work, we focus on the Contextual Bandit problem with linear rewards, under conditions of sparsity and batched data. We address the challenge of fairness by excluding irrelevant features from decision-making processes using a novel algorithm, Online Batched Sequential Inclusion (OBSI), which sequentially includes features as confidence in their impact on the reward increases. Our experiments on synthetic data show the superior performance of OBSI compared to other algorithms in terms of regret, relevance of features used, and compute. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09199 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features Swiers, Rowan Prabanantham, Subash Maher, Andrew Machine Learning Multi-armed Bandits (MABs) are increasingly employed in online platforms and e-commerce to optimize decision making for personalized user experiences. In this work, we focus on the Contextual Bandit problem with linear rewards, under conditions of sparsity and batched data. We address the challenge of fairness by excluding irrelevant features from decision-making processes using a novel algorithm, Online Batched Sequential Inclusion (OBSI), which sequentially includes features as confidence in their impact on the reward increases. Our experiments on synthetic data show the superior performance of OBSI compared to other algorithms in terms of regret, relevance of features used, and compute. |
| title | Batched Online Contextual Sparse Bandits with Sequential Inclusion of Features |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.09199 |