March 6, 2024, 5:41 a.m. | Yingrong Wang, Anpeng Wu, Haoxuan Li, Weiming Liu, Qiaowei Miao, Ruoxuan Xiong, Fei Wu, Kun Kuang

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.02624v1 Announce Type: new
Abstract: This paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a higher dosage of medication might increase the speed of a patient's recovery (short-term) but could also result in severe long-term side effects. Although recent works have investigated the problems about short-term or long-term effects or the both, how to …

abstract arxiv conflict cs.ai cs.lg effects example identify long-term paper pareto policy speed total treatment type

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