Tensor Regression Using Low-rank and Sparse Tucker Decompositions

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Tensor Regression Using Low-rank and Sparse Tucker Decompositions

9 November 2019

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Papers citing "Tensor Regression Using Low-rank and Sparse Tucker Decompositions"

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Title
Information-Theoretic Guarantees for Recovering Low-Rank Tensors from Symmetric Rank-One Measurements Eren C. Kızıldağ 118 0 0 07 Feb 2025
On High-dimensional and Low-rank Tensor Bandits Chengshuai Shi Cong Shen N. Sidiropoulos 76 2 0 06 May 2023
Fast and Provable Tensor Robust Principal Component Analysis via Scaled Gradient Descent Harry Dong Tian Tong Cong Ma Yuejie Chi 89 14 0 18 Jun 2022
Tensor-on-Tensor Regression: Riemannian Optimization, Over-parameterization, Statistical-computational Gap, and Their Interplay Yuetian Luo Anru R. Zhang 106 20 0 17 Jun 2022
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements Tian Tong Cong Ma Ashley Prater-Bennette Erin E. Tripp Yuejie Chi 108 33 0 29 Apr 2021
Low-rank Tensor Estimation via Riemannian Gauss-Newton: Statistical Optimality and Second-Order Convergence Yuetian Luo Anru R. Zhang 159 20 0 24 Apr 2021
Reduced-Rank Tensor-on-Tensor Regression and Tensor-variate Analysis of Variance Carlos Llosa-Vite R. Maitra CML 86 20 0 18 Dec 2020
Tensor Completion by Multi-Rank via Unitary Transformation Guang-Jing Song Michael K. Ng Xiongjun Zhang 61 13 0 16 Dec 2020