南京大学计算机软件新技术国家重点实验室
摘
要:
Machine
learning methods have demonstrated great success in many fields, but most of
them are lack of interpretability and stability. Causal inference is a powerful
modeling tool for explanatory analysis, which might enable current machine
learning to make an explainable and stable prediction. In this
talk, we will show some new challenges of estimating causal effect in the wild
big data scenarios, including (1) high dimensional and noisy variables, and (2)
differentiation among variables. To address these challenges, we proposed
Data-Driven Variable Decomposition (D2VD) and Differentiated Confounder
Balancing (DCB) algorithms. Moreover, by marrying causal inference with machine
learning, we proposed a causal regularizer to recover the causations between
predictors and response variable and designed a stable learning algorithm for
stable prediction across unknown testing data.
报告人简介:
Kun Kuang is a
Ph.D. candidate in the department of computer science at Tsinghua University.
He was a visiting researcher at Graduate School of Business, Stanford
University. His main research interests include data-driven causal analysis,
high dimensional inference, and interpretable and stable learning. He has
published several papers in top data mining and machine learning
conferences/journals of the relevant field such as SIGKDD, AAAI, and ICDM, etc.
时间:5月9日
14:40-15:20
地点:计算机科学技术楼221室
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