Title: Machine Learning for Personalized ClusteringSpeaker: Yisong Yue (Computing and Mathematical Sciences, California Institute of Technology)
Date: Tuesday, October 21
Room: NSH 3305
How would you browse and organize attractions for a potential trip to Paris? How would you organize research articles while conducting a literature review? Such sensemaking tasks are often facilitated via rich user interfaces that enable users to interact more meaningfully with the data. In this talk, I will focus on the problem of learning personalized user models from rich user interactions, and in particular from clustering interactions (i.e., grouping recommended items into clusters). Clustering interactions enable users to express similarity or redundancy between different items, and are a natural way for users to organize and group information. I will describe a new machine learning setting, called collaborative clustering. Analogous to collaborative filtering, in collaborative clustering the goal is to leverage how existing users cluster or group items in order to predict similarity models for other users’ clustering tasks. I will also describe some (very) preliminary work on modeling the full closed-loop interactive clustering setting.
Yisong Yue is an assistant professor in the Computing and Mathematical Sciences Department at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign. Yisong's research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for spatiotemporal reasoning, structured prediction, interactive learning systems, and learning with humans in the loop. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, sports analytics, policy learning in robotics, and adaptive routing & allocation problems.