Contextual Bandits with Budgeted Information Reveal
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914712155848704 |
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| author | Gan, Kyra Keyvanshokooh, Esmaeil Liu, Xueqing Murphy, Susan |
| author_facet | Gan, Kyra Keyvanshokooh, Esmaeil Liu, Xueqing Murphy, Susan |
| contents | Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice, clinicians have a limited budget to encourage patients to take these actions and collect additional information. We introduce a novel optimization and learning algorithm to address this problem. This algorithm effectively combines the strengths of two algorithmic approaches in a seamless manner, including 1) an online primal-dual algorithm for deciding the optimal timing to reach out to patients, and 2) a contextual bandit learning algorithm to deliver personalized treatment to the patient. We prove that this algorithm admits a sub-linear regret bound. We illustrate the usefulness of this algorithm on both synthetic and real-world data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_18511 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Contextual Bandits with Budgeted Information Reveal Gan, Kyra Keyvanshokooh, Esmaeil Liu, Xueqing Murphy, Susan Machine Learning Optimization and Control Contextual bandit algorithms are commonly used in digital health to recommend personalized treatments. However, to ensure the effectiveness of the treatments, patients are often requested to take actions that have no immediate benefit to them, which we refer to as pro-treatment actions. In practice, clinicians have a limited budget to encourage patients to take these actions and collect additional information. We introduce a novel optimization and learning algorithm to address this problem. This algorithm effectively combines the strengths of two algorithmic approaches in a seamless manner, including 1) an online primal-dual algorithm for deciding the optimal timing to reach out to patients, and 2) a contextual bandit learning algorithm to deliver personalized treatment to the patient. We prove that this algorithm admits a sub-linear regret bound. We illustrate the usefulness of this algorithm on both synthetic and real-world data. |
| title | Contextual Bandits with Budgeted Information Reveal |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2305.18511 |