Kaizheng Wang

I am an assistant professor in the Department of Industrial Engineering and Operations Research and a member of the Data Science Institute at Columbia University. My research interests lie at the intersection of statistics, machine learning and optimization. E-mail: kaizheng.wang [@] columbia [DOT] edu Google Scholar Resume

Openings

I'm seeking highly motivated Ph.D. students with strong mathematical backgrounds and interests in statistics, machine learning and optimization.

News

• Postdoc Zhongyuan Lyu is on the academic job market. Check out our new paper “Adaptive Transfer Clustering: A Unified Framework” on arXiv.

• I will be a meta-reviewer of COLT 2025.

• Paper “Learning Gaussian Mixtures Using the Wasserstein-Fisher-Rao Gradient Flow” accepted by the Annals of Statistics.

• New paper “Distribution-Free Predictive Inference under Unknown Temporal Drift” posted on arXiv.

• Paper “Model Assessment and Selection under Temporal Distribution Shift” accepted by ICML 2024. Congratulations to undergraduate student Elise Han on her first paper!

• Our proposal on policy evaluation, transfer learning and the safety of autonomous vehicles got awarded the Data Science Institute Seed Fund by Columbia University.

• Our paper on implicit regularization in nonconvex statistical estimation received the SIAM Activity Group on Imaging Science Best Paper Prize.

• I will be an Area Chair of ICML 2024.

• New paper “A Stability Principle for Learning under Non-Stationarity” posted on arXiv. Congratulations to Chengpiao Huang on his first paper!

• Our team (Chengpiao Huang, Yuhang Wu, and myself) won the Second Place Award in the 2023 INFORMS Blue Summit Supplies Data Challenge, along with a $1,000 cash prize. We also clinched the top spot in the two-week live test of pricing and forecasting with our own data integration techniques (Papers 1 and 2). Special thanks to the INFORMS Data Mining Society and Blue Summit Supplies for putting together this wonderful competition!

• I will be a meta-reviewer of COLT 2024.

• Undergraduate student Yuhang Wu started his PhD at the Decision, Risk, and Operations (DRO) division at Columbia Business School. Congratulations!

• New paper “Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift” posted on arXiv.

• I will be an Area Chair of ICML 2023.

• Undergraduate student Sara Zhao received the prestigious Stephen D. Guarino Memorial Award, which is made annually to one member of the senior class of IEOR. Congratulations!

• I will be an Area Chair of NeurIPS 2022.

Representative Publications

(-: author names are sorted alphabetically; : student/postdoc supervised.)

• Adaptive Transfer Clustering: A Unified Framework.
  Yuqi Gu, Zhongyuan Lyu, Kaizheng Wang (-)
  arXiv preprint arXiv:2410.21263, 2024.      Code
  
  

• A Stability Principle for Learning under Non-Stationarity.
  Chengpiao Huang, Kaizheng Wang (-)
  arXiv preprint arXiv:2310.18304, 2023.      Code
  
  

• Pseudo-Labeling for Kernel Ridge Regression under Covariate Shift.
  Kaizheng Wang
  Junior Researcher Award, 2024 ICSA China Conference.
  arXiv preprint arXiv:2302.10160, 2023.      Talk      Code
  
  

• Adaptive and Robust Multi-Task Learning
  Yaqi Duan, Kaizheng Wang (-)
  Annals of Statistics 51(5): 2015-2039, 2023.
  arXiv      Code
  
  

• Clustering a Mixture of Gaussians with Unknown Covariance
  Damek Davis, Mateo Díaz, Kaizheng Wang (-)
  Bernoulli, Accepted.
  arXiv preprint arXiv:2110.01602, 2021.
  
  

• An Theory of PCA and Spectral Clustering
  Emmanuel Abbe, Jianqing Fan, Kaizheng Wang (-)
  Annals of Statistics 50(4): 2359-2385, 2022.
  Frontiers of Science Award in Mathematics, 2024 International Congress of Basic Science.
  Presented by Jianqing Fan at the IMS Le Cam Lecture at the 2021 Joint Statistical Meetings.
  arXiv
  
  

• Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank
  Emmanuel Abbe, Jianqing Fan, Kaizheng Wang, Yiqiao Zhong (-)
  Annals of Statistics 48(3): 1452-1474, 2020.
  arXiv
  
  

• Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval, Matrix Completion and Blind Deconvolution
  Cong Ma, Kaizheng Wang, Yuejie Chi, Yuxin Chen
  Foundations of Computational Mathematics 20 (3): 451–632, 2020.
  SIAM Activity Group on Imaging Science Best Paper Prize, 2024.
  arXiv
  
  

• Distributed Estimation of Principal Eigenspaces
  Jianqing Fan, Dong Wang, Kaizheng Wang, Ziwei Zhu (-)
  Annals of Statistics 47 (6): 3009-3031, 2019.
  arXiv
  
  

• Spectral Method and Regularized MLE are both Optimal for Top-K Ranking
  Yuxin Chen, Jianqing Fan, Cong Ma, Kaizheng Wang (-)
  Annals of Statistics 47 (4): 2204-2235, 2019.
  arXiv
  
  

Funding

Support from the following funding sources are gratefully acknowledged:

• National Science Foundation (NSF) Grant DMS-2210907. Title: “Statistical and Computational Tools for Analyzing High-Dimensional Heterogeneous Data”. Duration: Aug 2022 – Jul 2025. Role: PI.

• Columbia University Data Science Institute Seed Fund. Title: “Policy Evaluation with Transfer Learning: How to assess safety performance of self-driving cars in NYC?”. Duration: Mar 2024 - Feb 2025. Role: Co-PI.

Miscellaneous

My Erdös number is 3.