John Wright

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MIT ∙ Columbia University

Associate Professor at Columbia University

Generalized Approach to Matched Filtering using Neural Networks

Gravitational wave science is a pioneering field with rapidly evolving d...
04/08/2021 ∙ by Jingkai Yan, et al. ∙ 6 ∙ share

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Deep Networks from the Principle of Rate Reduction

This work attempts to interpret modern deep (convolutional) networks fro...
10/27/2020 ∙ by Kwan Ho Ryan Chan, et al. ∙ 16 ∙ share

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Quantum soundness of the classical low individual degree test

Low degree tests play an important role in classical complexity theory, ...
09/27/2020 ∙ by Zhengfeng Ji, et al. ∙ 0 ∙ share

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Deep Networks and the Multiple Manifold Problem

We study the multiple manifold problem, a binary classification task mod...
08/25/2020 ∙ by Sam Buchanan, 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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Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications

The problem of finding the sparsest vector (direction) in a low dimensio...
01/20/2020 ∙ by Qing Qu, et al. ∙ 46 ∙ share

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MIP*=RE

We show that the class MIP* of languages that can be decided by a classi...
01/13/2020 ∙ by Zhengfeng Ji, et al. ∙ 0 ∙ share

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Gemmini: An Agile Systolic Array Generator Enabling Systematic Evaluations of Deep-Learning Architectures

Advances in deep learning and neural networks have resulted in the rapid...
11/22/2019 ∙ by Hasan Genc, et al. ∙ 0 ∙ 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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Complete Dictionary Learning via ℓ^4-Norm Maximization over the Orthogonal Group

This paper considers the fundamental problem of learning a complete (ort...
06/06/2019 ∙ by Yuexiang Zhai, et al. ∙ 0 ∙ share

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NEEXP in MIP*

We study multiprover interactive proof systems. The power of classical m...
04/11/2019 ∙ by Anand Natarajan, 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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A Geometric Analysis of Phase Retrieval

Can we recover a complex signal from its Fourier magnitudes? More genera...
02/22/2016 ∙ by Ju Sun, et al. ∙ 0 ∙ share

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Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method

We consider the problem of recovering a complete (i.e., square and inver...
11/15/2015 ∙ by Ju Sun, et al. ∙ 0 ∙ share

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Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture

We consider the problem of recovering a complete (i.e., square and inver...
11/11/2015 ∙ by Ju Sun, et al. ∙ 0 ∙ share

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When Are Nonconvex Problems Not Scary?

In this note, we focus on smooth nonconvex optimization problems that ob...
10/21/2015 ∙ by Ju Sun, et al. ∙ 0 ∙ share

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Complete Dictionary Recovery over the Sphere

We consider the problem of recovering a complete (i.e., square and inver...
04/26/2015 ∙ by Ju Sun, et al. ∙ 0 ∙ share

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Finding a sparse vector in a subspace: Linear sparsity using alternating directions

Is it possible to find the sparsest vector (direction) in a generic subs...
12/15/2014 ∙ 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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Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

Recovering a low-rank tensor from incomplete information is a recurring ...
07/22/2013 ∙ 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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Sparsity and Robustness in Face Recognition

This report concerns the use of techniques for sparse signal representat...
11/03/2011 ∙ by John Wright, et al. ∙ 0 ∙ share

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John Wright

Featured Co-authors

Yonina C. Eldar

Rene Vidal

Xiao Li

Ion Stoica

Yi Ma

Zhihui Zhu

Chong You

Ju Sun

Zhenyu Liao

Qing Qu

Yuqian Zhang

Generalized Approach to Matched Filtering using Neural Networks

Deep Networks from the Principle of Rate Reduction

Quantum soundness of the classical low individual degree test

Deep Networks and the Multiple Manifold Problem

From Symmetry to Geometry: Tractable Nonconvex Problems

Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications

MIP*=RE

Gemmini: An Agile Systolic Array Generator Enabling Systematic Evaluations of Deep-Learning Architectures

Short-and-Sparse Deconvolution -- A Geometric Approach

Complete Dictionary Learning via ℓ^4-Norm Maximization over the Orthogonal Group

NEEXP in MIP*

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

A Geometric Analysis of Phase Retrieval

Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method

Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture

When Are Nonconvex Problems Not Scary?

Complete Dictionary Recovery over the Sphere

Finding a sparse vector in a subspace: Linear sparsity using alternating directions

Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods

Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery

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

Sparsity and Robustness in Face Recognition