Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent

18 May 2020

Papers citing "Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent"

22 / 22 papers shown

Title
Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization G. Zhang S. Fattahi Richard Y. Zhang 40 34 0 13 Apr 2025
Matrix Completion with Graph Information: A Provable Nonconvex Optimization Approach Yao Wang Yiyang Yang Kaidong Wang Shanxing Gao Xiuwu Liao 60 0 0 12 Feb 2025
Learnable Scaled Gradient Descent for Guaranteed Robust Tensor PCA Lanlan Feng Ce Zhu Yipeng Liu Saiprasad Ravishankar Longxiu Huang 37 0 0 20 Jan 2025
Structured Sampling for Robust Euclidean Distance Geometry Chandra Kundu Abiy Tasissa HanQin Cai 89 0 0 14 Dec 2024
Collaborative and Efficient Personalization with Mixtures of Adaptors Abdulla Jasem Almansoori Samuel Horváth Martin Takáč FedML 42 2 0 04 Oct 2024
OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition Stephen Zhang V. Papyan VLM 43 1 0 20 Sep 2024
Efficient Low-rank Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization Hao Wang Ye Wang Xiangyu Yang 26 0 0 22 Jun 2024
Computational and Statistical Guarantees for Tensor-on-Tensor Regression with Tensor Train Decomposition Zhen Qin Zhihui Zhu 57 4 0 10 Jun 2024
On the Robustness of Cross-Concentrated Sampling for Matrix Completion HanQin Cai Longxiu Huang Chandra Kundu Bowen Su 32 4 0 28 Jan 2024
Provably Accelerating Ill-Conditioned Low-rank Estimation via Scaled Gradient Descent, Even with Overparameterization Cong Ma Xingyu Xu Tian Tong Yuejie Chi 16 9 0 09 Oct 2023
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA Yuchen Zhou Yuxin Chen 38 4 0 10 Mar 2023
Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing Jikai Jin Zhiyuan Li Kaifeng Lyu S. Du Jason D. Lee MLT 36 34 0 27 Jan 2023
Nonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective Yuetian Luo Nicolas García Trillos 17 6 0 29 Sep 2022
Optimal tuning-free convex relaxation for noisy matrix completion Yuepeng Yang Cong Ma 24 8 0 12 Jul 2022
Supervised Dictionary Learning with Auxiliary Covariates Joo-Hyun Lee Hanbaek Lyu W. Yao 16 1 0 14 Jun 2022
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification G. Zhang S. Fattahi Richard Y. Zhang 40 23 0 07 Jun 2022
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection HanQin Cai Jialin Liu W. Yin 25 39 0 11 Oct 2021
Nonconvex Factorization and Manifold Formulations are Almost Equivalent in Low-rank Matrix Optimization Yuetian Luo Xudong Li Anru R. Zhang 23 9 0 03 Aug 2021
GNMR: A provable one-line algorithm for low rank matrix recovery Pini Zilber B. Nadler 40 13 0 24 Jun 2021
Recursive Importance Sketching for Rank Constrained Least Squares: Algorithms and High-order Convergence Yuetian Luo Wen Huang Xudong Li Anru R. Zhang 19 15 0 17 Nov 2020
Escaping Saddle Points in Ill-Conditioned Matrix Completion with a Scalable Second Order Method C. Kümmerle C. M. Verdun 11 6 0 07 Sep 2020
Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently Laixi Shi Yuejie Chi 16 26 0 25 Nov 2019