FOUNDATIONS OF DATA SCIENCE SEMINAR SERIES
Probabilistic generative models are flexible tools on revealing the latent structures underlying complex data and performing top-down inference to generate observations. However, the bottom-up recognition ability is often not fully explored. An MLE estimate often leads to inferior prediction accuracy than discriminative methods. In this talk, I will introduce max-margin learning for probabilistic generative models, under a general framework of regularized Bayesian inference (RegBayes), where a posterior regularization term is defined to encourage a large-margin separation between true categories and alternatives, without sacrificing the generative capability. The basic idea is illustrated by examples on learning topic models and deep generative models. I will also talk about an online learning version of RegBayes for dealing with streaming data and large datasets.
Professor Jun Zhu
Dr. Jun Zhu is an associate professor at Department of Computer Science and Technology, Tsinghua University, and an adjunct faculty at Machine Learning Department, Carnegie Mellon University. He received his Ph.D. in Computer Science from Tsinghua in 2009. Before joining Tsinghua in 2011, he did post-doctoral research in CMU. His research interest lies in developing scalable machine learning methods to understand complex scientific and engineering data. Dr. Zhu has published over 60 peer-reviewed papers in the prestigious conferences and journals. He is an associate editor for IEEE Trans. on PAMI. He served as area chair for ICML, NIPS, UAI, IJCAI and AAAI. He was a local chair of ICML 2014. He is a recipient of the IEEE Intelligent Systems "AI's 10 to Watch" Award, NSFC Excellent Young Scholar Award, and CCF Young Scientist Award. His work is supported by the "221 Basic Research Plan for Young Talents" at Tsinghua.