In this talk, I will present a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve a regression error that is close to the optimal. We also verify the effectiveness of the proposed algorithm by an empirical study.

Bio: Rong Jin focuses his research on statistical machine learning and its application to information retrieval. He has worked on a variety of machine learning algorithms and their application to information retrieval, including retrieval models, collaborative filtering, cross lingual information retrieval, document clustering, and video/image retrieval. He has published over 160 conference and journal articles on related topics. He is currently an associate editor of ACM Transaction on Data Mining and Knowledge Discovery (KDD). Dr. Jin holds a Ph.D. in Computer Science from Carnegie Mellon University He received the NSF Career Award in 2006.