Slides of recent work
Preprints awaiting publication :-)
Muhang Tian, Bernie Chen, Allan Guo, Shiyi Jiang, Anru R. Zhang (2023+), Fast and reliable generation of EHR time series via diffusion models .
Jing Lei, Anru R. Zhang, Zihan Zhu (2023+), Computational and statistical thresholds in multi-layer stochastic block models .
Pixu Shi, Cameron Martino, Rungang Han, Stefan Janssen, Gregory Buck, Myrna Serrano, Kouros Owzar, Rob Knight, Liat Shenhav, Anru R Zhang (2023+), Time-informed dimensionality reduction for longitudinal microbiome studies .
Runshi Tang, Ming Yuan, and Anru R. Zhang (2023+), Mode-wise principal subspace pursuit and matrix spiked covariance model .
Yuetian Luo and Anru R. Zhang (2023+), Tensor-on-tensor regression: Riemannian optimization, over-parameterization, statistical-computational gap, and their interplay . [R Package ]
Joshua Agterberg and Anru R. Zhang (2023+), Estimating higher-order mixed memberships via the l_{2,\infty} tensor perturbation bound .
Joshua Agterberg and Anru R. Zhang (2023+), Statistical inference for low-rank tensors: Heteroskedasticity, subgaussianity, and applications .
Ziang Chen, Jianfeng Lu, and Anru R. Zhang (2023+), One-dimensional tensor network recovery .
Yuetian Luo, Xudong Li, and Anru R. Zhang (2023+), Nonconvex factorization and manifold formulations are almost equivalent in low-rank matrix optimization .
Publications
Shiyi Jiang, Xin Gai, Miriam Treggiari, William Stead, Yuankang Zhao, David Page, Anru R. Zhang (2023), Soft phenotyping for sepsis via EHR time-aware soft clustering , Journal of Biomedical Informatics , to appear.
Ilias Diakonikolas, Daniel Kane, Yuetian Luo, and Anru R. Zhang (2023). Statistical and computational limits for tensor-on-tensor association detection , Proceedings of Thirty Sixth Conference on Learning Theory (COLT) , 195, 5260-5310.
Shiyi Jiang, Rungang Han, Krishnendu Chakrabarty, David Page, William Stead, and Anru R. Zhang (2023). Timeline registration for electronic health records , AMIA Summits on Translational Science Proceedings , 291-299.
(This paper won the Data Science Distinguished Paper Award from 2023 AMIA Informatics Summit . Only one paper receives this award.)
Yuetian Luo and Anru R. Zhang (2023). Low-rank tensor estimation via Riemannian Gauss-Newton: Statistical optimality and second-order convergence , Journal of Machine Learning Research , to appear.
Yuetian Luo, Xudong Li, Wen Huang, and Anru R. Zhang (2023). Recursive importance sketching for rank constrained least squares , Operations Research , to appear.
Peter Hoff, Andrew McCormack, and Anru R. Zhang (2023). Core shrinkage covariance estimation for matrix-variate data , Journal of the Royal Statistical Society, Series B , to appear.
Rungang Han and Anru R. Zhang (2023). Discussion of "Vintage factor analysis with Varimax performs statistical inference" , Journal of the Royal Statistical Society, Series B , to appear.
Rungang Han, Pixu Shi, and Anru R. Zhang (2023). Guaranteed functional tensor singular value decomposition , Journal of the American Statistical Association , to appear.
Sitan Chen, Jerry Li, Yuanzhi Li, and Anru R. Zhang (2023). Learning polynomial transformations , 2023 Annual ACM Symposium on Theory of Computing (STOC) , to appear.
Sampling is as easy as learning the score: Theory for diffusion models with minimal data assumptions (with Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, and Anru R. Zhang), 2023 International Conference on Learning Representations (ICLR) , accept: notable-top-5%.
Phase transition for detecting a small community in a large network (Jiashun Jin, Tracy Ke, Paxton Turner, and Anru R. Zhang), 2023 International Conference on Learning Representations (ICLR) , accepted.
On geometric connections of embedded and quotient geometries in Riemannian fixed-rank matrix optimization (Yuetian Luo, Xudong Li, and Anru R. Zhang), Mathematics of Operations Research , to appear.
Learning good state and action representations for Markov decision process via tensor decomposition (Chengzhuo Ni, Yaqi Duan, Munther Dahleh, Mengdi Wang, and Anru R. Zhang), Journal of the Machine Learning Research , to appear.
A short version was presented at International Symposium on Information Theory (ISIT) , 2021.
Exact clustering in tensor block model: Statistical optimality and computational limit (Rungang Han, Yuetian Luo, Miaoyan Wang, and Anru R. Zhang), Journal of the Royal Statistical Society, Series B , to appear. [R Package ]
(This paper received the Student's Paper Award from the Statistical Learning and Data Science Section of the American Statistical Association, 2021)
Inference for low-rank tensors -- No need to debias (Dong Xia, Anru R. Zhang, and Yuchen Zhou), The Annals of Statistics , to appear.
Tensor clustering with planted structures: Statistical optimality and computational limits (Yuetian Luo and Anru R. Zhang), The Annals of Statistics , 50, 584-613, 2022.
Heteroskedastic PCA: Algorithm, optimality, and applications (Tony Cai, Yihong Wu, and Anru R. Zhang), The Annals of Statistics , 50, 53-80, 2022.
An optimal statistical and computational framework for generalized tensor estimation (Rungang Han, Rebecca Willett, and Anru R. Zhang), The Annals of Statistics , 50, 1-29, 2022.
On the non-asymptotic concentration of heteroskedastic Wishart-type matrix (Tony Cai, Rungang Han, and Anru R. Zhang), Electronic Journal of Probability , 27, 1-40.
Optimal high-order tensor SVD via tensor-train orthogonal iteration (Yuchen Zhou, Lili Zheng, Yazhen Wang, and Anru R. Zhang), IEEE Transactions on Information Theory , 66, 5927-5964, 2022. [R Package ]
Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference (Tony Cai and Yuchen Zhou, and Anru R. Zhang), IEEE Transactions on Information Theory , 68, 5975-6002, 2022.
A Schatten-q low-rank matrix perturbation analysis via perturbation projection error bound (Yuetian Luo, Rungang Han, and Anru R. Zhang), Linear Algebra and Its Applications , 630, 225-240, 2021.
A sharp blockwise tensor perturbation bound for orthogonal iteration (Anru R. Zhang, Yuetian Luo, Garvesh Raskutti, and Ming Yuan), Journal of Machine Learning Research , 22, 1-48, 2021.
High-dimensional log-error-in-variable regression with applications to microbial compositional data analysis (Pixu Shi, Yuchen Zhou, Anru R. Zhang), Biometrika , 109, 405-420, 2022.
(This paper received Biometrics Early-Stage Investigator Award by the Biometrics Section of the American Statistical Association, 2019)
ISLET: fast and optimal low-rank tensor regression via importance sketchings (with Yuetian Luo, Garvesh Raskutti, and Ming Yuan), SIAM Journal on Mathematics of Data Science , 2, 444-479, 2020. [R package ]
Learning Markov models via low-rank optimization (with Ziwei Zhu, Xudong Li and Mengdi Wang), Operations Research , to appear.
Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health (with Kehui Chen), Statistica Sinica , 32, 345-365, 2022.
Open problem: Average-case hardness of hypergraphic planted clique detection (with Yuetian Luo), Conference on Learning Theory (COLT) , 125, 3852-3856, 2020. [talk and slides]
On the non-asymptotic and sharp tail bounds of random variables (with Yuchen Zhou), Stat , 9, e314, 2020.
Sparse and low-rank tensor estimation via cubic sketchings (with Botao Hao and Guang Cheng), IEEE Transactions on Information Theory , 66, 9, 2020.
A short version published in Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) , 2020.
Multi-sample estimation of bacterial composition matrix in metagenomics data (with Yuanpei Cao and Hongzhe Li), Biometrika , 107, 75-92, 2020.
Spectral state compression of Markov processes (with Mengdi Wang), IEEE Transactions on Information Theory , 66, 3202-3231, 2020.
Cross: Efficient low-rank tensor completion (single author), The Annals of Statistics , 47, 936-964, 2019. [R package ]
Optimal sparse singular value decomposition for high-dimensional high-order data (with Rungang Han), Journal of American Statistical Association , 114, 1708-1725, 2019. [R package ]
Semi-supervised inference: General theory and estimation of means (with Lawrence Brown and Tony Cai), The Annals of Statistics , 47, 2538-2566, 2019.
Tensor SVD: Statistical and computational limits (with Dong Xia), IEEE Transactions on Information Theory , 64, 1-28, 2018. [An R implementation ]
Sequential rerandomization (with Quan Zhou, Philip Ernst, Kari Lock Morgan, and Donald Rubin), Biometrika , 105, 745-752, 2018.
Rate-optimal perturbation bounds for singular subspaces with applications to high-Dimensional statistics (with Tony Cai), The Annals of Statistics , 46, 60-89, 2018.
Estimation of Markov chain via rank-constrained likelihood (with Mengdi Wang and Xudong Li), International Conference on Machine Learning (ICML) , PMLR 80:3033-3042, 2018.
Structured matrix completion with applications in genomic data integration (with Tianxi Cai and Tony Cai), Journal of American Statistical Association , 111, 621-633, 2016. [R package ]
Regression Analysis for Microbiome Compositional Data (with Pixu Shi and Hongzhe Li), The Annals of Applied Statistics , 10, 1019-1040, 2016. [Matlab package ]
Instrumental variables estimation with some invalid instruments and its application to Mendelian randomization (with Hyunseung Kang, Tony Cai and Dylan Small), Journal of American Statistical Association , 111, 132-144, 2016. [R Package ]
Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data (with Tony Cai), Journal of Multivariate Analysis , 150, 55-74, 2016.
Inference for high-dimensional differential correlation matrices (with Tony Cai), Journal of Multivariate Analysis , 143, 107-126, 2016.
ROP: matrix recovery via rank-one projections (with Tony Cai), The Annals of Statistics , 43, 102-138, 2015.
Sparse representation of a polytope and recovery of sparse signals and low-rank matrices (with Tony Cai), IEEE Transactions on Information Theory , 60, 122-132, 2014.
Sharp RIP bound for sparse signal and low-rank matrix recovery (with Tony Cai), Applied and Computational Harmonic Analysis , 35, 74-93, 2013.
Compressed sensing and affine rank minimization under restricted isometry (with Tony Cai), IEEE Transactions on Signal Processing , 61, 3279-3290, 2013.
Collaborative Research
Anru R. Zhang, Ryan Bell, Chen An, Runshi Tang, Shana Hall, Cliburn Chan, Al-Khalil, Kareem, Meade, Christina (2023). Cocaine use prediction with tensor-based machine learning on multimodal MRI connectome data , Neural Computation , to appear.
Aditi U. Gurkar, Akos A. Gerencser, Ana L. Mora, Andrew C. Nelson, Anru R. Zhang, et al. Spatial mapping of cellular senescence: emerging challenges and opportunities , Nature Aging , 3, 776-790, 2023.
Sheri Towe, Runshi Tang, Matt Gibson, Anru R. Zhang, Christina Meade, Longitudinal changes in neurocognitive performance related to drug use intensity in a sample of persons with and without HIV who use stimulants , Drug And Alcohol Dependence , to appear.
Christine Park, Beiyu Liu, Stephen C. Harward, Anru R. Zhang, et al., Ventriculomegaly and postoperative intraventricular blood predict cerebrospinal fluid diversion following posterior fossa tumor resection , Journal of Neurosurgery: Pediatrics , 28, 533-543, 2021.
Chenyu Zhang, Rungang Han, Anru Zhang, and Paul Voyles, Denoising Atomic Resolution 4D Scanning Transmission Electron Microscopy Data with Tensor Singular Value Decomposition , Ultramicroscopy , 219, 113123, 2020.
Wan, C. et al. LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data Nucleic Acids Research , 2019.
Other Manuscripts