378 citations · 2.2k across the 69 of their papers we have counts for
8 papers · 1 filter
Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently
Laixi Shi, Yuejie Chi
Multi-channel sparse blind deconvolution, or convolutional sparse coding, refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple…
Subspace Estimation from Unbalanced and Incomplete Data Matrices: Statistical Guarantees
Changxiao Cai, Gen Li, Yuejie Chi +2
This paper is concerned with estimating the column space of an unknown low-rank matrix , given noisy and partial observatio…
On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution
Maxime Ferreira Da Costa, Yuejie Chi
The stability of spike deconvolution, which aims at recovering point sources from their convolution with a point spread function (PSF), is known to be related to the separation bet…
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction
Boyue Li, Shicong Cen, Yuxin Chen +1
There is growing interest in large-scale machine learning and optimization over decentralized networks, e.g. in the context of multi-agent learning and federated learning. Due to t…
Vector-Valued Graph Trend Filtering with Non-Convex Penalties
Rohan Varma, Harlin Lee, Jelena Kovačević +1
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. W…
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data
Shicong Cen, Huishuai Zhang, Yuejie Chi +2
Stochastic variance reduced methods have gained a lot of interest recently for empirical risk minimization due to its appealing run time complexity. When the data size is large and…
Harnessing Sparsity over the Continuum: Atomic Norm Minimization for Super Resolution
Yuejie Chi, Maxime Ferreira Da Costa
Convex optimization recently emerges as a compelling framework for performing super resolution, garnering significant attention from multiple communities spanning signal processing…
Noisy Matrix Completion: Understanding Statistical Guarantees for Convex Relaxation via Nonconvex Optimization
Yuxin Chen, Yuejie Chi, Jianqing Fan +2
This paper studies noisy low-rank matrix completion: given partial and noisy entries of a large low-rank matrix, the goal is to estimate the underlying matrix faithfully and effici…