all AI news
Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
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
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
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
MLOps Engineer - Hybrid Intelligence
@ Capgemini | Madrid, M, ES
Analista de Business Intelligence (Industry Insights)
@ NielsenIQ | Cotia, Brazil