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07/02/2023
Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model
This paper introduces a novel framework called Mode-wise Principal Subsp...
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03/09/2023
Phase transition for detecting a small community in a large network
How to detect a small community in a large network is an interesting pro...
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09/26/2022
Self-supervised Denoising via Low-rank Tensor Approximated Convolutional Neural Network
Noise is ubiquitous during image acquisition. Sufficient denoising is of...
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09/22/2022
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
We provide theoretical convergence guarantees for score-based generative...
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07/25/2022
Core Shrinkage Covariance Estimation for Matrix-variate Data
A separable covariance model for a random matrix provides a parsimonious...
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07/21/2022
One-dimensional Tensor Network Recovery
We study the recovery of the underlying graphs or permutations for tenso...
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06/17/2022
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay
We study the tensor-on-tensor regression, where the goal is to connect t...
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05/20/2022
Discussion of "Vintage factor analysis with Varimax performs statistical inference"
We wholeheartedly congratulate Drs. Rohe and Zeng for their insightful p...
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04/08/2022
Learning Polynomial Transformations
We consider the problem of learning high dimensional polynomial transfor...
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10/23/2021
On Geometric Connections of Embedded and Quotient Geometries in Riemannian Fixed-rank Matrix Optimization
In this paper, we propose a general procedure for establishing the lands...
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08/09/2021
Guaranteed Functional Tensor Singular Value Decomposition
This paper introduces the functional tensor singular value decomposition...
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08/03/2021
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization
In this paper, we consider the geometric landscape connection of the wid...
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04/24/2021
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence
In this paper, we consider the estimation of a low Tucker rank tensor fr...
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12/29/2020
Inference for Low-rank Tensors – No Need to Debias
In this paper, we consider the statistical inference for several low-ran...
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12/18/2020
Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit
High-order clustering aims to identify heterogeneous substructure in mul...
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11/17/2020
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence
In this paper, we propose a new ecursive mportance ketching algorithm fo...
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10/06/2020
Optimal High-order Tensor SVD via Tensor-Train Orthogonal Iteration
This paper studies a general framework for high-order tensor SVD. We pro...
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09/12/2020
Open Problem: Average-Case Hardness of Hypergraphic Planted Clique Detection
We note the significance of hypergraphic planted clique (HPC) detection ...
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08/28/2020
On the Non-Asymptotic Concentration of Heteroskedastic Wishart-type Matrix
This paper focuses on the non-asymptotic concentration of the heterosked...
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08/06/2020
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration
In this paper, we develop novel perturbation bounds for the high-order o...
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08/04/2020
A Schatten-q Matrix Perturbation Theory via Perturbation Projection Error Bound
This paper studies the Schatten-q error of low-rank matrix estimation by...
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05/21/2020