Anru Zhang

Orcid: 0000-0002-8721-5252

According to our database1, Anru Zhang authored at least 42 papers between 2013 and 2024.

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Bibliography

2024
Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data.
Neural Comput., January, 2024

2023
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition.
J. Mach. Learn. Res., 2023

Fast and Reliable Generation of EHR Time Series via Diffusion Models.
CoRR, 2023

Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model.
CoRR, 2023

Learning Polynomial Transformations via Generalized Tensor Decompositions.
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Phase transition for detecting a small community in a large network.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Statistical and Computational Limits for Tensor-on-Tensor Association Detection.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
Optimal High-Order Tensor SVD via Tensor-Train Orthogonal Iteration.
IEEE Trans. Inf. Theory, 2022

Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference.
IEEE Trans. Inf. Theory, 2022

Learning Markov Models Via Low-Rank Optimization.
Oper. Res., 2022

Self-supervised Denoising via Low-rank Tensor Approximated Convolutional Neural Network.
CoRR, 2022

One-dimensional Tensor Network Recovery.
CoRR, 2022

Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay.
CoRR, 2022

Learning Polynomial Transformations.
CoRR, 2022

Provable Second-Order Riemannian Gauss-Newton Method for Low-Rank Tensor Estimation <sup>‖</sup>.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration.
J. Mach. Learn. Res., 2021

On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization.
CoRR, 2021

Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization.
CoRR, 2021

Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence.
CoRR, 2021

Learning Good State and Action Representations via Tensor Decomposition.
Proceedings of the IEEE International Symposium on Information Theory, 2021

2020
Spectral State Compression of Markov Processes.
IEEE Trans. Inf. Theory, 2020

Sparse and Low-Rank Tensor Estimation via Cubic Sketchings.
IEEE Trans. Inf. Theory, 2020

ISLET: Fast and Optimal Low-Rank Tensor Regression via Importance Sketching.
SIAM J. Math. Data Sci., 2020

Inference for Low-rank Tensors - No Need to Debias.
CoRR, 2020

Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence.
CoRR, 2020

A Schatten-q Matrix Perturbation Theory via Perturbation Projection Error Bound.
CoRR, 2020

Tensor Clustering with Planted Structures: Statistical Optimality and Computational Limits.
CoRR, 2020

An Optimal Statistical and Computational Framework for Generalized Tensor Estimation.
CoRR, 2020

Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection.
Proceedings of the Conference on Learning Theory, 2020

2018
Tensor SVD: Statistical and Computational Limits.
IEEE Trans. Inf. Theory, 2018

A Non-asymptotic, Sharp, and User-friendly Reverse Chernoff-Cramèr Bound.
CoRR, 2018

State Compression of Markov Processes via Empirical Low-Rank Estimation.
CoRR, 2018

Estimation of Markov Chain via Rank-constrained Likelihood.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Guaranteed Tensor PCA with Optimality in Statistics and Computation.
CoRR, 2017

2016
Minimax rate-optimal estimation of high-dimensional covariance matrices with incomplete data.
J. Multivar. Anal., 2016

Inference for high-dimensional differential correlation matrices.
J. Multivar. Anal., 2016

Cross: Efficient Low-rank Tensor Completion.
CoRR, 2016

2014
Sparse Representation of a Polytope and Recovery of Sparse Signals and Low-Rank Matrices.
IEEE Trans. Inf. Theory, 2014

2013
Compressed Sensing and Affine Rank Minimization Under Restricted Isometry.
IEEE Trans. Signal Process., 2013

Sharp RIP Bound for Sparse Signal and Low-Rank Matrix Recovery
CoRR, 2013

ROP: Matrix Recovery via Rank-One Projections.
CoRR, 2013


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