{"id":205,"date":"2023-02-16T19:51:59","date_gmt":"2023-02-16T19:51:59","guid":{"rendered":"https:\/\/meetings.informs.org\/wordpress\/phoenix2023\/?page_id=205"},"modified":"2023-09-18T00:24:59","modified_gmt":"2023-09-18T00:24:59","slug":"tutorials","status":"publish","type":"page","link":"https:\/\/meetings.informs.org\/wordpress\/phoenix2023\/tutorials\/","title":{"rendered":"TutORials"},"content":{"rendered":"\n
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The TutORials in Operations Research<\/em> series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meeting.<\/p> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Simulation Optimization in the New Era of AI<\/strong><\/span><\/h3>\n

We review simulation optimization methods and discuss how these methods underpin modern artificial intelligence (AI) techniques. In particular, we focus on three areas: stochastic gradient estimation, which plays a central role in training neural networks for deep learning and reinforcement learning; simulation sample allocation, which can be used as the node selection policy in Monte Carlo tree search; and variance reduction, which can accelerate training procedures in AI.<\/p>\n

Speakers: Yiijie Peng, Chun-Hung Chen, and Michael Fu<\/strong><\/p> <\/div>\n<\/div>\n\n

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