Learning and Decision-Making from Rank Data (Lirong Xia)

Abstract

Decision-making from rank 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 a unified approach from statistics, economics, and computation. In this talk I will focus on learning. I will discuss our recent theoretical and algorithmic progresses in efficient learning of random utility models and their mixtures, which are among the most well-applied statistical models for rank data.

Time

Speaker

Lirong Xia, Rensselaer Polytechnic Institute

Room