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

24 April 2021

Papers citing "Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence"

7 / 7 papers shown

Title
Information-Theoretic Guarantees for Recovering Low-Rank Tensors from Symmetric Rank-One Measurements Eren C. Kızıldağ 63 0 0 07 Feb 2025
Time Series Generative Learning with Application to Brain Imaging Analysis Zhenghao Li Sanyou Wu Long Feng MedIm 41 0 0 19 Jul 2024
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition Zhen Qin Zhihui Zhu 74 4 0 10 Jun 2024
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization Cong Ma Xingyu Xu Tian Tong Yuejie Chi 18 9 0 09 Oct 2023
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay Yuetian Luo Anru R. Zhang 29 19 0 17 Jun 2022
Tensor Principal Component Analysis in High Dimensional CP Models Yuefeng Han Cun-Hui Zhang 31 10 0 10 Aug 2021
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence Yuetian Luo Wen Huang Xudong Li Anru R. Zhang 23 15 0 17 Nov 2020