个人简历

教育经历\r
2002-2006 美国加州大学伯克利分校统计专业 博士\r
1998-2002 北京大学数学专业 学士\r
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工作经历\r
2006 至今 清华大学经济管理学院 助理教授、副教授、长聘副教授

研究领域

"复杂数据驱动的决策与预测、人工智能与经济社会"

近期论文

会议论文（计算机／数据科学）\r
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Measure the Predictive Heterogeneity (with Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang and Peng Cui). ICLR, 2023\r
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Factual Observation Based Heterogeneity Learning for Counterfactual Prediction (with Hao Zou, Haotian Wang, Renzhe Xu, Jian Pei, Junjian Ye and Peng Chi). CLeaR (Conference on Causal Learning and Reasoning), 2023.\r
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Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation (with Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqin Zhu, Yuxuan Liu, Furui Liu, Zhihua Wang and Fei Wu), AAAI, 2023, CCF中国计算机学会A类\r
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Product Ranking for Revenue Maximization with Multiple Purchases (with Renzhe Xu, Xingxuan Zhang, Yafeng Zhang, Xiaolong Chen and Peng Cui), NeurIPS, 2022, CCF中国计算机学会A类\r
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Distributionally Robust Optimization with Data Geometry (with Jiashuo Liu, Jiayun Wu, Peng Cui), NeurIPS, 2022, CCF中国计算机学会A类\r
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Instrumental Variable Regression with Confounder Balancing (with Anpeng Wu, Kun Kuang and Fei Wu), ICML, 2022, CCF中国计算机学会A类\r
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Counterfactual Prediction for Outcome-Oriented Treatments (with Hao Zou, Peng Cui, Jiangang Han, Shuiping Chen and Xuetao Ding), ICML, 2022, CCF中国计算机学会A类\r
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Regulatory Instruments for Fair Personalized Pricing (with Renzhe Xu, Xingxuan Zhang, Peng Cui, Zheyan Shen and Jiazheng Xu), WWW, 2022, CCF中国计算机学会A类\r
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Kernelized Heterogeneous Risk Minimization (with Jiashuo Liu, Zheyuan Hu, Peng Cui and Zheyan Shen), NeurIPS, 2021, CCF中国计算机学会A类\r
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Heterogeneous Risk Minimization (with Jiashuo Liu, Zheyuan Hu, Peng Cui and Zheyan Shen), ICML, (Spotlight) 2021, CCF中国计算机学会A类\r
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Invariant Adversarial Learning for Distributional Robustness (with Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang and Yishi Lin), AAAI, 2021, CCF中国计算机学会A类\r
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Counterfactual Prediction for Bundle Treatment (with Hao Zou, Peng Cui, Zheyan Shen, Jianxin Ma, Hongxia Yang and Yue He), NeurIPS, 2020, CCF中国计算机学会A类\r
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Algorithmic Decision Making with Conditional Fairness (with Renzhe Xu, Peng Cui, Kun Kuang, Lingjun Zhou, Zheyan Shen and Wei Cui), KDD , 2020, CCF中国计算机学会A类\r
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Stable Learning via Differentiated Variable Decorrelation (with Zheyan Shen, Peng Cui, Jiashuo Liu, Tong Zhang and Zhitang Chen), KDD, 2020, CCF中国计算机学会A类\r
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Stable Prediction with Model Misspecification and Agnostic Distribution Shift (with Kun Kuang, Ruoxuan Xiong, Peng Cui and Susan Athey), AAAI , 2020, CCF中国计算机学会A类\r
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Causally Regularized Learning On Data with Agnostic Bias (with Zheyan Shen, Peng Cui and Kun Kuang), ACM MM (oral presentation), 2018, CCF中国计算机学会A类\r
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Stable Prediction across Unknown Environments (with Kun Kuang, Peng Cui, Susan Athey and Ruoxuan Xiong), KDD (long presentation), 2018, CCF中国计算机学会A类\r
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Estimating Causal Effects in the Wild via Differentiated Confounder Balancing (with Kun Kuang, Peng Cui, Meng Jiang and Shiqiang Yang), KDD (oral presentation), 2017, CCF中国计算机学会A类\r
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Treatment Effect Estimation with Data-Driven Variable Decomposition (with Kun Kuang, Peng Cui, Meng Jiang, Shiqiang Yang and Fei Wang), AAAI, 2017, CCF中国计算机学会A类\r
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How Out-of-Pocket Ratio Influences Readmission: An Analysis Based on Front Sheet of Inpatient Medical Record (with Luo He, Xiaolei Xie and Hongyan Liu), ICSH 2017, LNCS 10347, pp.67-78, 2017\r
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期刊论文（英文, SCI/SSCI）\r
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Distributionally Robust Optimization with Stable Adversarial Training (with Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou and Kun Kuang), IEEE Transactions on Knowledge and Data Engineering (TKDE), to appear\r
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Stable Prediction with Leveraging Seed Variable (with Kun Kuang, Haotian Wang, Yue Liu, Ruoxuan Xiong, Weiming Lu, Runze Wu, Yueting Zhuang, Fei Wu and Peng Cui), IEEE Transactions on Knowledge and Data Engineering (TKDE), to appear\r
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Differentiated Matching for Individual and Average Treatment Effect Estimation, (with Ziyu Zhao, Kun Kuang, Peng Cui, Runze Wu, Jun Xiao and Fei Wu), Data Mining and Knowledge Discovery , to appear\r
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Learning Decomposed Representations for Treatment Effect Estimation (with Anpeng Wu, Junkun Yuan, Kun Kuang, Runze Wu, Qiang Zhu, Yueting Zhuang and Fei Wu), IEEE Transactions on Knowledge and Data Engineering (TKDE), to appear\r
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Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition (with Junkun Yuan, Anpeng Wu, Kun Kuang, Runze Wu, Fei Wu and Lanfen Lin), ACM Transactions on Knowledge Discovery from Data (TKDD) , 2022\r
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Continuous Treatment Effect Estimation via Generated Adversarial de-Confounding, (with Kun Kuang, Yunzhe Li, Peng Cui, Hongxia Yang, Jianrong Tao and Fei Wu), Data Mining and Knowledge Discovery , 2021\r
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Data-driven Variable Decomposition for Treatment Effect Estimation (with Kun Kuang, Peng Cui, Hao Zou, Jianrong Tao, Fei Wu and Shiqiang Yang), IEEE Transactions on Knowledge and Data Engineering (TKDE) , to appear (A shorter version appeared on AAAI2017)\r
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Cross-Estimation for Decision Selection (with Xinyue Gu), Applied Stochastic Models in Business and Industry, 2020\r
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Treatment Effect Estimation via Differentiated Confounder Balancing and Regression (with Kun Kuang, Peng Cui, Meng Jiang and Shiqiang Yang), ACM Transactions on Knowledge Discovery from Data (TKDD) , (A shorter version appeared on KDD2017), Vol.14, No.1, 6:1-6:25, 2020\r
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On Estimation of Partially Linear Varying-Coefficient Transformation Models with Censored Data (with Baosheng Liang, Xingwei Tong and Jianguo Sun), Statistica Sinica, 22, 1963-1975, 2019\r
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A Discrete Spatial Model for Wafer Yield Prediction (with Hao Wang, Seung Hoon Tong, In Kap Chang and Kaibo Wang), Quality Engineering, Vol.30, Issue 2, 169-182, 2018\r
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Hierarchical Models for the Spatial-Temporal Carbon Nanotube Height Variations (with Jialing Tao, Kaibo Wang, Liang Liu and Qi Cai), International Journal of Production Research, Vol. 54, No. 21, 6613-6632, 2016\r
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A Spatial Variable Selection Method for Monitoring Product Surface (with Kaibo Wang and Wei Jiang), International Journal of Production Research, Vol. 54, No. 14, 4161-4181, 2016\r
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Counterfactual Decomposition of Movie Star Effects with Star Selection (with Angela Liu and Tridib Mazumdar), Management Science, Vol.61, No.7, pp.1704-1721, 2015\r
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Simultaneous Monitoring of Process Mean Vector and Covariance Matrix via Penalized Likelihood Estimation (with Kaibo Wang and Arthur Yeh), Computational Statistics and Data Analysis, 78, 206-217, 2014\r
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Trends in China's Gender Employment and Pay Gap: Estimating Gender Pay Gaps with Employment Selection (with Wei Chi), Journal of Comparative Economics, 42, 708-725, 2014\r
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Monitoring Covariance Matrix via Penalized Likelihood Estimation (with Kaibo Wang and Arthur Yeh), IIE Transactions, 45, 132-146, 2013\r
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Monitoring Multivariate Process Variability with Individual Observations via Penalized Likelihood Estimation (with Arthur Yeh and Kaibo Wang), International Journal of Production Research, Vol. 50, No. 22, 6624-6638, 2012\r
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Forward Adaptive Banding for Estimating Large Covariance Matrices (with Chenlei Leng), Biometrika, 94, 4, pp.821-830, 2011 (统计学四大顶尖期刊之一)\r
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Decomposition of the increase in earnings inequality in urban China: A distributional approach (with Wei Chi and Qiumei Yu), China Economic Review, 22, 299-312, 2011\r
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Least Squares Approximations With a Diverging Number of Parameters (with Chenlei Leng), Statistics and Probability Letters, 80, 254-261, 2010\r
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Asymptotically Distribution-Free Goodness-of-Fit Testing: A Unifying View, Econometric Reviews, 28(6):632-657, 2009\r
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Shrinkage tuning parameter selection with a diverging number of parameters (with Hansheng Wang and Chenlei Leng), Journal of the Royal Statistical Society, Series B (Statistical Methodology), 71, Part 3, pp. 671-683, 2009 (统计学四大顶尖期刊之一)\r
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Glass ceiling or sticky floor? Examining the gender earnings differential across the earnings distribution in urban China, 1987–2004 (with Wei Chi), Journal of Comparative Economics, 36, 243-263, 2008 (此论文获得首届麦肯锡中国经济学奖)\r
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Nonparametric Testing of An Exclusion Restriction in Quantile Regression, Communications in Statistics—Theory and Methods, 37: 2877-2889, 2008\r
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Regularization in statistics (with discussion and rejoinder, with Peter Bickel), Test, Vol. 15, No. 2, pp. 271-344, 2006\r
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文集章节（英文）\r
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Curse of Dimensionality Revisited: Collapse of the Particle Filter in Very Large Scale Systems (with Thomas Bengtsson and Peter Bickel), IMS Collections, Probability and Statistics: Essays in Honor of David A. Freedman, Vol. 2, 316-334, 2008\r
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Sharp Failure Rates for the Bootstrap Particle Filter in High Dimensions (with Peter Bickel and Thomas Bengtsson), IMS Collections, Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh, Vol. 3, 318-329, 2008\r
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Local Polynomial Regression on Unknown Manifolds (with Peter Bickel), IMS Lecture Notes–Monograph Series, Complex Datasets and Inverse Problems: Tomography, Networks and Beyond, Vol.54, 177-186, Vol. 54, 177-186, 2007