Yuhang Cai†, Kangjie Zhou†, Jingfeng Wu, Song Mei, Michael Lindsey, Peter Bartlett (2025).
Implicit Bias of Gradient Descent for Non-Homogeneous Deep Networks.
The 42th International Conference on Machine Learning (ICML 2025).
Jingyang Lyu, Kangjie Zhou, Yiqiao Zhong (2025).
A statistical theory of overfitting for imbalanced classification.
arXiv preprint.
Shuangping Li*, Tselil Schramm*, Kangjie Zhou* (2024).
Discrepancy Algorithms for the Binary Perceptron.
The 57th Annual ACM Symposium on Theory of Computing (STOC 2025).
Andrea Montanari*, Kangjie Zhou* (2024).
Which exceptional low-dimensional projections of a Gaussian point cloud
can be found in polynomial time?
Annals of Probability, to appear.
Fangyi Chen*, Yunxiao Chen*, Zhiliang Ying*, Kangjie Zhou* (2024).
Dynamic Factor Analysis of High-dimensional Recurrent Events.
Biometrika, 2025.
Yuchen Wu*, Kangjie Zhou* (2024).
Sharp Analysis of Power Iteration for Tensor PCA.
Journal of Machine Learning Research, 2024.
Andrea Montanari*, Yiqiao Zhong*, Kangjie Zhou* (2024).
Tractability from overparametrization: The example of the negative perceptron.
Probability Theory and Related Fields, 2024.
Raphaël Berthier*, Andrea Montanari*, Kangjie Zhou* (2023).
Learning time-scales in two-layers neural networks.
Foundations of Computational Mathematics, 2024.
Yuchen Wu*, Kangjie Zhou* (2023).
Lower bounds for the convergence of tensor power iteration on random
overcomplete models.
The 36th Annual Conference on Learning Theory (COLT 2023).
Andrea Montanari*, Kangjie Zhou* (2022).
Overparametrized linear dimensionality reductions: From projection
pursuit to two-layer neural networks.
arXiv preprint.
Kangjie Zhou, Andrea Montanari (2022).
High-dimensional projection pursuit: Outer bounds and applications to
interpolation in neural networks.
The 35th Annual Conference on Learning Theory (COLT 2022).
Kangjie Zhou, Jinzhu Jia (2021).
Propensity score adapted covariate selection for causal inference.
arXiv preprint.
* Alphabetical, † Equal contribution