[PAST EVENT] Efficient Decision-Making and Learning from Big Ranking Data
Title: Efficient Decision-Making and Learning from Big Ranking Data
Lirong Xia, RPI
Decision-making with ranking data is ubiquitous in our life: voters rank candidates in elections, search engines rank websites based on keywords, e-commerce websites recommend items based on users' information and behavior. The fundamental challenge is: How can we make better decisions by learning from big ranking data?
My research tackles this multi-disciplinary challenge by taking a unified approach of statistics, machine learning, and economics. For learning, I will talk about our recent theoretical and algorithmic progresses in efficient learning of mixtures of random utility models, which are arguably the most well-established statistical models for ranking data. For decision-making, I will talk about the design and analysis of decision-making mechanisms w.r.t. computational efficiency, statistical efficiency, and economic efficiency such as fairness and strategy-proofness.
Bio: Lirong Xia is an assistant professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). Prior to joining RPI in 2013, he was a CRCS fellow and NSF CI Fellow at the Center for Research on Computation and Society at Harvard University. He received his Ph.D. in Computer Science and M.A. in Economics from Duke University. His research focuses on the intersection of computer science and microeconomics. He is an associate editor of Mathematical Social Sciences and is on the editorial board of Journal of Artificial Intelligence Research. He is the recipient of an NSF CAREER award, a Simons-Berkeley Research Fellowship, and was named as one of "AI's 10 to watch" by IEEE Intelligent Systems.