[{"id":20394,"title":"Learning to Simulate: Generative Metamodeling via Quantile Regression","permalink":"https:\/\/bschool.nus.edu.sg\/biz-events\/event\/learning-to-simulate-generative-metamodeling-via-quantile-regression\/","category":"Seminars and talks","event_dept":{"value":"analytics-operations","label":"Analytics & Operations"},"event_sec_dept":false,"event_details":{"event_start_date":"5  December  2024","event_end_date":"5  December  2024","event_start_time":"10:00 am","event_end_time":"11:00 am","event_dress_code":"NA"},"event_loc":{"eve_address_selection":"7","eve_location_1":{"eve_org":"NUS Business School","eve_build":"Mochtar Riady Building","eve_room":"3-2","eve_add":"15 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119245,"eve_map_url":"https:\/\/goo.gl\/maps\/Q1kyjwxHNE22"},"eve_location_2":{"eve_org":"Shaw Foundation Alumni House","eve_build":"","eve_room":"Clove and Lemongrass Room Level 2","eve_add":"11 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119244,"eve_map_url":"https:\/\/goo.gl\/maps\/docgThkDWFxKdb9c7"},"eve_location_3":{"eve_org":"Hon Sui Sen Memorial Library Auditorium","eve_build":"","eve_room":"","eve_add":"1 Hon Sui Sen Drive","eve_count":"Singapore","eve_copos":117588,"eve_map_url":"https:\/\/goo.gl\/maps\/NJjWK4RMpC92"},"eve_location_4":{"eve_org":"NUSS Kent Ridge Guild House","eve_build":"","eve_room":"Dalvey Room","eve_add":"9 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119241,"eve_map_url":"https:\/\/goo.gl\/maps\/nXn2Luh96pH2"},"eve_location_5":{"eve_org":"Institute of Data Science","eve_build":"Innovation 4.0","eve_room":"1-3","eve_add":"3 Research Link","eve_count":"Singapore","eve_copos":117602,"eve_map_url":"https:\/\/goo.gl\/maps\/i1xocvvDh27QUXem7"},"eve_location_6":{"eve_org":"","eve_build":"","eve_room":"","eve_add":"","eve_count":"","eve_copos":"","eve_map_url":""},"eve_location_7":"E1-07-21\/22 - ISEM Executive Classroom"},"event_introduction":"","event_short_intro":"","event_topic":null,"event_banner":false,"event_external_url":"","event_registration_details":{"event_registration_form":false,"event_registration_message":"","event_registration_deadline":null,"eve_registration_url":"","event_form":"","event_registration_ack":""},"event_speaker":[{"event_speaker_name":"Xiaowei Zhang","event_speaker_designation":"Associate Professor","event_speaker_affiliation":"Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology","event_speaker_picture":false,"event_speaker_url":"","event_speaker_introduction":"<p>Xiaowei Zhang is an Associate Professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. He earned his Ph.D. in Management Science and Engineering in 2011 and M.S. in Financial Mathematics in 2010, both from Stanford University, and his B.S. in Mathematics in 2006 from Nankai University. His research focuses on methodological advances in stochastic simulation and optimization, decision analytics, and reinforcement learning, with applications in service operations management, financial technology, and digital economy. He currently serves as an Associate Editor for Management Science and Operations Research.<\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":"<p>Stochastic simulation models effectively capture complex system dynamics but are often too slow for real-time decision-making. Traditional metamodeling techniques learn relationships between simulator inputs and a single output summary statistic, such as the mean or median. These techniques enable real-time predictions without additional simulations. However, they require prior selection of one appropriate output summary statistic, limiting their flexibility in practical applications. We propose a new concept: generative metamodeling. It aims to construct a &#8220;fast simulator of the simulator,&#8221; generating random outputs significantly faster than the original simulator while preserving approximately equal conditional distributions. Generative metamodels enable rapid generation of numerous random outputs upon input specification, facilitating immediate computation of any summary statistic for real-time decision-making. We introduce a new algorithm, quantile-regression-based generative metamodeling (QRGMM), and establish its distributional convergence and convergence rate. Extensive numerical experiments demonstrate QRGMM&#8217;s efficacy compared to other state-of-the-art generative algorithms in practical real-time decision-making scenarios.<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"","event_enq_department":"","event_enq_email":"","event_enq_telephone":"","event_enq_website":""}}]