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

17 June 2022

Papers citing "Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay"

11 / 11 papers shown

Title
Robust Low-rank Tensor Train Recovery Zhen Qin Zhihui Zhu 32 1 0 19 Oct 2024
Efficient Decision Trees for Tensor Regressions Hengrui Luo Akira Horiguchi Li Ma 25 4 0 04 Aug 2024
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition Zhen Qin Zhihui Zhu 57 4 0 10 Jun 2024
Factor Augmented Tensor-on-Tensor Neural Networks Guanhao Zhou Yuefeng Han Xiufan Yu 27 1 0 30 May 2024
Conformalized Tensor Completion with Riemannian Optimization Hu Sun Yang Chen 19 2 0 01 May 2024
Near-Optimal differentially private low-rank trace regression with guaranteed private initialization Mengyue Zha 38 0 0 24 Mar 2024
Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent Zhiyu Liu Zhi-Long Han Yandong Tang Xi-Le Zhao Yao Wang 27 1 0 22 Jan 2024
Computationally Efficient and Statistically Optimal Robust High-Dimensional Linear Regression Yinan Shen Jingyang Li Jian-Feng Cai Dong Xia 10 1 0 10 May 2023
Elliptically-Contoured Tensor-variate Distributions with Application to Improved Image Learning Carlos Llosa-Vite R. Maitra 8 1 0 13 Nov 2022
Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery Lijun Ding Liwei Jiang Yudong Chen Qing Qu Zhihui Zhu 18 28 0 23 Sep 2021
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence Yuetian Luo Anru R. Zhang 40 18 0 24 Apr 2021