Yuqian Zhang

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Columbia University ∙ cornell university ∙ University of California, San Diego

Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle

Existing results for low-rank matrix recovery largely focus on quadratic...
08/31/2020 ∙ by Lijun Ding, et al. ∙ 0 ∙ share

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From Symmetry to Geometry: Tractable Nonconvex Problems

As science and engineering have become increasingly data-driven, the rol...
07/14/2020 ∙ by Yuqian Zhang, et al. ∙ 12 ∙ share

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Structures of Spurious Local Minima in k-means

k-means clustering is a fundamental problem in unsupervised learning. Th...
02/16/2020 ∙ by Wei Qian, et al. ∙ 0 ∙ share

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Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning

This paper develops new methods to recover the missing entries of a high...
12/15/2019 ∙ by Jicong Fan, et al. ∙ 0 ∙ share

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Analysis of the Optimization Landscapes for Overcomplete Representation Learning

We study nonconvex optimization landscapes for learning overcomplete rep...
12/05/2019 ∙ by Qing Qu, et al. ∙ 8 ∙ share

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Short-and-Sparse Deconvolution -- A Geometric Approach

Short-and-sparse deconvolution (SaSD) is the problem of extracting local...
08/28/2019 ∙ by Yenson Lau, et al. ∙ 7 ∙ share

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Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities

This work studies the location estimation problem for a mixture of two r...
06/16/2019 ∙ by Wei Qian, et al. ∙ 0 ∙ share

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High-dimensional semi-supervised learning: in search for optimal inference of the mean

We provide a high-dimensional semi-supervised inference framework focuse...
02/02/2019 ∙ by Yuqian Zhang, et al. ∙ 0 ∙ share

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On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution

Blind deconvolution is the problem of recovering a convolutional kernel ...
01/07/2019 ∙ by Yuqian Zhang, et al. ∙ 0 ∙ share

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Geometry and Symmetry in Short-and-Sparse Deconvolution

We study the Short-and-Sparse (SaS) deconvolution problem of recovering ...
01/02/2019 ∙ by Han-wen Kuo, et al. ∙ 0 ∙ share

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Structured Local Optima in Sparse Blind Deconvolution

Blind deconvolution is a ubiquitous problem of recovering two unknown si...
06/01/2018 ∙ by Yuqian Zhang, et al. ∙ 0 ∙ share

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Convolutional Phase Retrieval via Gradient Descent

We study the convolutional phase retrieval problem, which considers reco...
12/03/2017 ∙ by Qing Qu, et al. ∙ 0 ∙ share

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Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods

Recovering matrices from compressive and grossly corrupted observations ...
03/29/2014 ∙ by Cun Mu, et al. ∙ 0 ∙ share

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Toward Guaranteed Illumination Models for Non-Convex Objects

Illumination variation remains a central challenge in object detection a...
07/04/2013 ∙ by Yuqian Zhang, et al. ∙ 0 ∙ share

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Efficient Point-to-Subspace Query in ℓ^1: Theory and Applications in Computer Vision

Motivated by vision tasks such as robust face and object recognition, we...
11/05/2012 ∙ by Ju Sun, et al. ∙ 0 ∙ share

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Efficient Point-to-Subspace Query in ℓ^1 with Application to Robust Object Instance Recognition

Motivated by vision tasks such as robust face and object recognition, we...
08/02/2012 ∙ by Ju Sun, et al. ∙ 0 ∙ share

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Yuqian Zhang

Featured Co-authors

Yonina C. Eldar

Xiao Li

Yudong Chen

Madeleine Udell

John Wright

Jelena Bradic

Ju Sun

Zhihui Zhu

Qing Qu

Donald Goldfarb

Pengcheng Zhou

Low-rank matrix recovery with non-quadratic loss: projected gradient method and regularity projection oracle

From Symmetry to Geometry: Tractable Nonconvex Problems

Structures of Spurious Local Minima in k-means

Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning

Analysis of the Optimization Landscapes for Overcomplete Representation Learning

Short-and-Sparse Deconvolution -- A Geometric Approach

Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities

High-dimensional semi-supervised learning: in search for optimal inference of the mean

On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution

Geometry and Symmetry in Short-and-Sparse Deconvolution

Structured Local Optima in Sparse Blind Deconvolution

Convolutional Phase Retrieval via Gradient Descent

Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods

Toward Guaranteed Illumination Models for Non-Convex Objects

Efficient Point-to-Subspace Query in ℓ^1: Theory and Applications in Computer Vision

Efficient Point-to-Subspace Query in ℓ^1 with Application to Robust Object Instance Recognition