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首页 >> 新闻公告 >> 学术报告 >> 12月27日(周二)下午3:40卡内基梅隆大学Kun Zhang博士学术报告--Causality and Learning
12月27日(周二)下午3:40卡内基梅隆大学Kun Zhang博士学术报告--Causality and Learning
2016-12-21 18:00

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报告题目:Causality and Learning

报告人: Dr. Kun Zhang

单位:Carnegie Mellon University, USA

时间:20161227日周二下午3:40

地点:中心楼二楼 自动化学院教育部重点实验室会议室

邀请人:孙长银 教授

主持人:孙长银 教授

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欢迎各位老师和研究生参加!

 

摘要

Can we find the causal direction between two variables? How can we make optimal predictions in the presence of distribution shift? We are often faced with such causal modeling or prediction problems in various disciplines. Recently, with the rapid accumulation of huge volumes of data, both causal discovery, i.e., learning causal information from purely observational data, and machine learning are seeing exciting opportunities as wells great challenges. In this talk I will focus on recent advances in causal discovery and how causal information facilitates understanding and solving certain problems of learning from heterogeneous data.

In particular, I will talk about conditional independence-based and functional causal model-based approaches to causal discovery, including their underlying assumptions, algorithms, and applications. Practical issues in causal discovery, including selection bias, nonstationarity or heterogeneity of the data, and high-dimensionality of the problem, will also be addressed. Finally, I will discuss why and how underlying causal knowledge helps in learning from heterogeneous data when the i.i.d. assumption is dropped, with transfer learning? as a particular example.

 

报告人简介

Kun Zhang is an assistant professor in the philosophy department and the machine learning department (affiliated) of Carnegie Mellon University (CMU), USA, and a senior research scientist at Max Planck Institute for Intelligent Systems, Germany. His main research interests include causal analysis, machine learning, artificial intelligence, computational finance, and large-scale data analysis. He has made a series of contributions in solving some long-standing problems in causality, such as how to distinguish cause from effect and how to make nonparametric conditional independence test reliable. He has served as a senior program committee member or area chair for a number of conferences in machine learning or artificial intelligence, and organized various academic activities to foster interdisciplinary research in causality.

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