Efficient Decision-Making and Learning from Big Ranking Data

Decision-making with ranking data is ubiquitous in our life: voters rank candidates in elections, search engines rank websites based on their relevance to input 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.

Speaker 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 PhD in Computer Science and MA 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.

If you would like to meet with Lirong while he is visiting, or come out to lunch with us after his talk, please contact John Dickerson at john [-at-] cs [dot] umd [dot] edu.