Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution under Random Designs

4 August 2020

Papers citing "Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution under Random Designs"

5 / 5 papers shown

Title
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA Yuchen Zhou Yuxin Chen 40 4 0 10 Mar 2023
Optimal tuning-free convex relaxation for noisy matrix completion Yuepeng Yang Cong Ma 28 8 0 12 Jul 2022
Robust Matrix Completion with Heavy-tailed Noise Bingyan Wang Jianqing Fan 21 4 0 09 Jun 2022
Spectral Methods for Data Science: A Statistical Perspective Yuxin Chen Yuejie Chi Jianqing Fan Cong Ma 42 165 0 15 Dec 2020
Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently Laixi Shi Yuejie Chi 30 26 0 25 Nov 2019