Work

Contextual Bandits with Budgeted Information Reveal

Year: 2023

Type: article

Abstract: 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.

Source: PubMed

DOI: 10.48550/arxiv.2305.18511

Authors: Gan, Kyra, Keyvanshokooh, Esmaeil, Liu, Xueqing, Murphy, Susan

Institutions: Harvard University, Duke-NUS Medical School, Cornell Tech, Texas A&M University

Language: English

Topic: Advanced Bandit Algorithms Research

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute on Drug Abuse, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research