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07/09/2022
Error Analysis of Tensor-Train Cross Approximation
Tensor train decomposition is widely used in machine learning and quantu...
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06/11/2021
Landscape Correspondence of Empirical and Population Risks in the Eigendecomposition Problem
Spectral methods include a family of algorithms related to the eigenvect...
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10/29/2019
Support Recovery for Sparse Signals with Non-stationary Modulation
The problem of estimating a sparse signal from low dimensional noisy obs...
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07/11/2019
The Landscape of Non-convex Empirical Risk with Degenerate Population Risk
The landscape of empirical risk has been widely studied in a series of m...
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04/22/2019
Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems
The (global) Lipschitz smoothness condition is crucial in establishing t...
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03/16/2019
Spherical Principal Component Analysis
Principal Component Analysis (PCA) is one of the most important methods ...
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02/14/2019
Atomic Norm Denoising for Complex Exponentials with Unknown Waveform Modulations
Non-stationary blind super-resolution is an extension of the traditional...
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02/13/2019
Simultaneous Sparse Recovery and Blind Demodulation
The task of finding a sparse signal decomposition in an overcomplete dic...
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11/07/2018
Global Optimality in Distributed Low-rank Matrix Factorization
We study the convergence of a variant of distributed gradient descent (D...
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05/24/2018
Stable Super-Resolution of Images
We study the ubiquitous problem of super-resolution in which one aims at...
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04/05/2017
Geometry of Factored Nuclear Norm Regularization
This work investigates the geometry of a nonconvex reformulation of mini...
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11/10/2015
Experimental robustness of Fourier Ptychography phase retrieval algorithms
Fourier ptychography is a new computational microscopy technique that pr...
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05/15/2015
Optimal Low-Rank Tensor Recovery from Separable Measurements: Four Contractions Suffice
Tensors play a central role in many modern machine learning and signal p...
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09/06/2012
