Time：10:00-11:30, 27 August
Venue：SOM A523
Speaker：Assistant Professor Yiding Feng, HKUST
Host: Assistant Professor Haotian Song, ZJU
Bio:
Yiding Feng is an Assistant Professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology (HKUST). Prior to joining HKUST, he was a Principal Researcher at the University of Chicago Booth School of Business and a Postdoctoral Researcher at Microsoft Research New England. He received his Ph.D. in Computer Science from Northwestern University in 2021 and his B.S. from the ACM Honors Class at Shanghai Jiao Tong University in 2016.His research interests lie at the intersection of operations research, economics and computation, and theoretical computer science. His work has been published in leading journals such as Management Science and Operations Research, as well as top theoretical computer science and economics conferences including STOC, FOCS, SODA, EC, ITCS, and WINE. He was a recipient of the INFORMS Auctions and Market Design (AMD) Michael H. Rothkopf Junior Researcher Paper Prize (Second Place) and the APORS Young Researcher Best Paper Award.
Abstract:
We introduce quasi-regular and quasi-MHR distributions as generalized families that relax classical regularity conditions while preserving economic intuitions, extending the parameterized λ-regular distributions to λ-quasi-regular versions. These new families offer enhanced mathematical tractability through natural order statistics properties, enable direct extensions of existing results (often with improved bounds), and lead to novel discoveries - most notably achieving 0.5 and 0.1908 approximations for one-sample prophet inequalities in symmetric/asymmetric revenue maximization settings, filling an important gap where only welfare maximization results previously existed. This work, done jointly with Yaonan Jin (Huawei TCS Lab), will appear at FOCS 2025.
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