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

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

4 August 2020

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

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Title
How robust is randomized blind deconvolution via nuclear norm minimization against adversarial noise? Julia Kostin Felix Krahmer Dominik Stöger 53 0 0 17 Mar 2023
Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA Yuchen Zhou Yuxin Chen 71 4 0 10 Mar 2023
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model Bingyan Wang Yuling Yan Jianqing Fan 89 20 0 28 May 2021
Spectral Methods for Data Science: A Statistical Perspective Yuxin Chen Yuejie Chi Jianqing Fan Cong Ma 153 173 0 15 Dec 2020
Bridging Convex and Nonconvex Optimization in Robust PCA: Noise, Outliers, and Missing Data Yuxin Chen Jianqing Fan Cong Ma Yuling Yan 86 52 0 15 Jan 2020
Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently Laixi Shi Yuejie Chi 86 26 0 25 Nov 2019
Nonconvex Matrix Factorization from Rank-One Measurements Yuanxin Li Cong Ma Yuxin Chen Yuejie Chi 68 51 0 17 Feb 2018