
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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Support Recovery for Sparse Signals with Nonstationary Modulation
The problem of estimating a sparse signal from low dimensional noisy obs...
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The Landscape of Nonconvex Empirical Risk with Degenerate Population Risk
The landscape of empirical risk has been widely studied in a series of m...
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Provable Bregmandivergence based Methods for Nonconvex and NonLipschitz Problems
The (global) Lipschitz smoothness condition is crucial in establishing t...
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Spherical Principal Component Analysis
Principal Component Analysis (PCA) is one of the most important methods ...
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Atomic Norm Denoising for Complex Exponentials with Unknown Waveform Modulations
Nonstationary blind superresolution is an extension of the traditional...
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Simultaneous Sparse Recovery and Blind Demodulation
The task of finding a sparse signal decomposition in an overcomplete dic...
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Global Optimality in Distributed Lowrank Matrix Factorization
We study the convergence of a variant of distributed gradient descent (D...
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Stable SuperResolution of Images
We study the ubiquitous problem of superresolution in which one aims at...
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Geometry of Factored Nuclear Norm Regularization
This work investigates the geometry of a nonconvex reformulation of mini...
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Experimental robustness of Fourier Ptychography phase retrieval algorithms
Fourier ptychography is a new computational microscopy technique that pr...
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Optimal LowRank 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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The Sample Complexity of Search over Multiple Populations
This paper studies the sample complexity of searching over multiple popu...
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Gongguo Tang
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