南京大学计算机科学与技术系

软件新技术与产业化协同创新中心

摘 要：

Many tasks in empirical sciences or engineering rely on the underlying causal information. As it is often difficult to carry out randomized experiments, inferring causal relations from purely observational data, known as causal discovery, has drawn much attention. Over the last few years, with the rapid accumulation of huge volumes of data, causal discovery is facing exciting opportunities but also great challenges. One feature such data often exhibit is distribution shift. In this talk, I will present a principled framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD).

In the second part of the talk, I will show how causal knowledge facilitates machine learning in the presence of distribution shifts, focusing on our two particular settings.  One is about specific and shared causal relation modeling and mechanism-based clustering. The other is about time-varying causal modeling and forecasting, where the causal coefficients follow dynamic models. Given the causal model, we treat prediction as a problem in Bayesian inference, which exploits the time-varying property of the data and adapts to new observations in a principled manner.

Biwei Huang (黄碧薇) is a Ph.D. candidate at Carnegie Mellon University, supervised by Prof. Kun Zhang and Prof. Clark Glymour. Her main research interests include causal discovery, machine learning, and computational neuroscience. She is actively exploring theoretical implementations of causal discovery, how causal knowledge facilitates learning problems, and practical uses of causality in neuroscience, biology, etc.

时间：12月20日星期五 14:00

地点：计算机科学技术楼230室