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    青年学者学术沙龙Causal Inference and Stable Learning
    发布时间:2019-05-05 15:53:42

     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


    上一篇:青年学者学术沙龙《高速分布式神经网络训练: 分分钟训练ImageNet》
    下一篇:青年学者学术沙龙Deep Learning for High-Level Visual Recognition
    友情链接:
    江苏省科学技术协会 中国计算机学会 南京大学 南京大学计算机科技与技术系 南京大学软件学院 东南大学计算机科学与工程学院 江苏经贸职业技术学院 南京信息职业技术学院 南京工业职业技术学院 江苏海事职业技术学院 常州信息职业技术学院 国网电力科学研究院 电子科技集团第28研究所 江南计算技术研究所 
       
     

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