My research is kindly supported by
- (2023-2026) Hong Kong RGC GRF Grant 16302323
- (2022-2025) Hong Kong RGC GRF Grant 16301622
- (2022) HKUST - GZU Joint Research Collaboration Fund
- (2021-2024) Hong Kong RGC GRF Grant 16300121
- (2020-2023) Hong Kong RGC GRF Grant 16303320
- (2020-2023) Hong Kong RGC GRF Grant 16302020 (transferred from Bing-Yi JING)
- (2019-2022) Hong Kong RGC ECS Grant 26302019
- (2019) Adobe Research Gift Award
- (2019-2020) HKUST-Webank joint project (Co-I, PI: Can YANG)
- (2018-) HKUST Start-up Grant
****** I am constantly looking for motivated Ph.D. students. Send me an email if interested.******
Technical reports
- Online Policy Learning and Inference by Matrix Completion (with Congyuan Duan and Jingyang Li), 2024
- Optimal Differentially Private PCA and Estimation for Spiked Covariance Matrices (with Tony Cai and Mengyue Zha), 2024
- Multiple Testing of Linear Forms for Noisy Matrix Completion (with Wanteng Ma, Lilun Du and Ming Yuan), 2023
- Optimal Clustering of Discrete Mixtures: Binomial, Poisson, Block Models, and Multi-layer Networks (with Zhongyuan Lyu and Ting li), 2023
- Quantile and pseudo-Huber Tensor Decomposition (with Yinan Shen), 2023
- rMultiNet: An R Package For Multilayer Networks Analysis (with Ting li, Zhongyuan Lyu and Chenyu Ren), 2023
- U-Statistic Reduction: Higher-Order Accurate Risk Control and Statistical-Computational Trade-Off, with Application to Network Method-of-Moments (with Meijia Shao and Yuan Zhang), 2023
- Online Tensor Learning: Computational and Statistical Trade-offs, Adaptivity and Optimal Regret (with Jian-Feng Cai and Jingyang Li), 2023
- Higher-order accurate two-sample network inference and network hashing (with Meijia Shao, Yuan Zhang, Qiong Wu and Shuo Chen), 2022
- Optimal Clustering by Lloyd Algorithm for Low-Rank Mixture Model (with Zhongyuan Lyu), 2022
- Computationally Efficient and Statistically Optimal Robust Low-rank Matrix Estimation (with Jian-Feng Cai, Jingyang Li and Yinan Shen), 2022
- Community Detection for Hypergraph Networks via Regularized Tensor Power Iteration (with Tracy Ke and Feng Shi), 2019
- Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model (with Tianxi Cai, Luwan Zhang and Doudou Zhou), 2019.
Publications
- High-dimensional Linear Bandits with Knapsacks (with Wanteng Ma and Jiashuo Jiang), ICML, 2024
- Optimal Regularized Online Allocation by Adaptive Re-Solving (with Wanteng Ma, Ying Cao and Danny H.K. Tsang), Operations Research, 2024+
- Semiparametric TEnsor Factor Analysis by Iteratively Projected SVD (with Elynn Y. Chen, Chencheng Cai and Jianqing Fan), Journal of Royal Statistical Socierty Series B, 2023+
- Optimal Estimation and Computational Limit of Low-rank Gaussian Mixtures (with Zhongyuan Lyu), The Annals of Statistics, vol. 51(2), 2023
- Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition (with Zhongyuan Lyu and Yuan Zhang), Journal of Computational and Graphical Statistics, 2023
- Provable Tensor-Train Format Tensor Completion by Riemannian Optimization (with Jian-Feng Cai and Jingyang Li), Journal of Machine Learning Research, vol. 23, 1-77, 2022
- Generalized Low-rank plus Sparse Tensor Estimation by Fast Riemannian Optimization (with Jian-Feng Cai and Jingyang Li), Journal of American Statistical Association, 2022
- Inference for Low-rank Tensors - No Need to Debias (with Anru Zhang and Yuchen Zhou), The Annals of Statistics, 50(2), 1220-1245, 2022
- Edgeworth expansions for network moments (with Yuan Zhang), The Annals of Statistics, 50(2), 726-753, 2022.
- Normal Approximation and Confidence Region of Singular Subspaces, Electronic Journal of Statistics,Vol.15,3798-3851, 2021. [Slides]
- Community Detection on Mixture Multi-layer Networks via Regularized Tensor Decomposition (with Bing-Yi Jing, Ting Li and Zhongyuan Lyu), The Annals of Statistics, 49(6), 3181-3205, 2021
- Statistical Inferences of Linear Forms for Noisy Matrix Completion (with Ming Yuan), [Slides], Journal of Royal Statistical Society Series B, 83(1), 58-77, 2021
- Effective Tensor Sketching via Sparsification (with Ming Yuan), IEEE Transactions on Information Theory, 67(2), 1356-1369, 2021
- Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries (with Ming Yuan and Cun-Hui Zhang), The Annals of Statistics, 49(1), 76-99, 2021.
- Confidence region of singular subspaces for high-dimensional and low-rank matrix regression IEEE Transactions on Information Theory, 2019
- Non-asymptotic bounds for percentiles of independent non-identical random variables Statistics & Probability Letters,(155):111-120, 2019
- The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising
(with Fan Zhou), The Journal of Machine Learning Research,(61):1-42, 2019.
- Tensor SVD: Statistical and Computational Limits. (with Anru Zhang), IEEE Transactions on Information Theory, 64(11): 7311-7338,2018.
- On Polynomial Time Methods for Exact Low Rank Tensor Completion.
(with Ming Yuan), Foundations of Computational Mathematics, 2019.
- Estimation of low rank density matrices by Pauli measurements.
Electronic Journal of Statistics, 11(1): 50-77, 2017.
- Variable Selection o f Linear Programming Discriminant Estimator Commnication in Statistics - Theory and Methods, 46(7): 3321-3341, 2017.
- Estimation of low rank density matrices: bounds in Schatten norms and other distances. (with Vladimir Koltchinskii) Electronic Journal of Statistics, 10(2):2717-2745,2016
- Perturbation of linear forms of singular vectors under Gaussian noise. (with Vladimir Koltchinskii) High Dimensional Probability VII, the Cargèse Volume,71: 397-423, 2016.
- Optimal Estimation of Low Rank Density Matrices. (with Vladimir Koltchinskii) The Journal of Machine Learning Research,16(1757-1792), 2015.