作者：DMIR 来自： 发表时间：2015-12-30 浏览量：1424
报告题目: Machine Learning and Causal Modeling: How They Benefit Each Other?
报告人 : 卡内基梅隆大学 Kun Zhang 博士
Kun Zhang is an assistant professor in the philosophy department at Carnegie Mellon University (CMU). Before joining CMU, he was a senior research scientist at Max-Planck Institute for Intelligent Systems, Germany, and a lead scientist at Information Sciences Institute, University of Southern California. He obtained his Ph.D from Chinese University of Hong Kong and then worked at University of Helsinki as a postdoctoral fellow. His main research interests include causal discovery, machine learning, and large-scale data analysis, and has organized various academic activities to foster interdisciplinary research in causality.
Recently causal discovery has benefited a great deal from statistics and machine learning, and on the other hand, causal information has been demonstrated to be able to facilitate understanding and solving certain machine learning problems. In this talk I will first discuss how conditional independence in random variables and the independent noise condition enable causal discovery, i.e., learning causal information from purely observational data. They lead to the so-called constraint-based and functional causal model-based approaches to causal discovery, respectively. I will illustrate the advantages and limitations of those approaches and give some real-world applications. Secondly, I will consider two machine learning problems--semi-supervised learning and domain adaptation--from a causal point of view, and briefly discuss why and how the underlying causal information helps to solve them.