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02/23/2021
Online Stochastic Gradient Descent Learns Linear Dynamical Systems from A Single Trajectory
This work investigates the problem of estimating the weight matrices of ...
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07/15/2020
Convexifying Sparse Interpolation with Infinitely Wide Neural Networks: An Atomic Norm Approach
This work examines the problem of exact data interpolation via sparse (n...
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06/30/2020
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
We consider the problem of factorizing a structured 3-way tensor into it...
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03/16/2019
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems
This paper proposes and analyzes a communication-efficient distributed o...
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02/28/2019
NOODL: Provable Online Dictionary Learning and Sparse Coding
We consider the dictionary learning problem, where the aim is to model t...
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02/26/2019
TensorMap: Lidar-Based Topological Mapping and Localization via Tensor Decompositions
We propose a technique to develop (and localize in) topological maps fro...
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02/26/2019
Target-based Hyperspectral Demixing via Generalized Robust PCA
Localizing targets of interest in a given hyperspectral (HS) image has a...
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02/26/2019
A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing
We consider the task of localizing targets of interest in a hyperspectra...
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02/21/2019
A Dictionary-Based Generalization of Robust PCA Part I: Study of Theoretical Properties
We consider the decomposition of a data matrix assumed to be a superposi...
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02/21/2019
A Dictionary Based Generalization of Robust PCA
We analyze the decomposition of a data matrix, assumed to be a superposi...
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06/13/2018
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
Our paper proposes a generalization error bound for a general family of ...
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06/13/2018
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
We study constrained nonconvex optimization problems in machine learning...
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09/20/2017
Near Optimal Sketching of Low-Rank Tensor Regression
We study the least squares regression problem _Θ∈S_ D,RAΘ-b_2, where S_...
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09/02/2017
Communication-efficient Algorithm for Distributed Sparse Learning via Two-way Truncation
We propose a communicationally and computationally efficient algorithm f...
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08/29/2017
Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring
This paper considers the problem of estimating an unknown high dimension...
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06/19/2017
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions
We propose a DC proximal Newton algorithm for solving nonconvex regulari...
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04/08/2017
Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor
In this paper we study the problem of noisy tensor completion for tensor...
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12/29/2016
Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization
We propose a general theory for studying the geometry of nonconvex objec...
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05/25/2016
A First Order Free Lunch for SQRT-Lasso
Many statistical machine learning techniques sacrifice convenient comput...
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05/09/2016
Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
We propose a stochastic variance reduced optimization algorithm for solv...
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02/24/2016
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
The measurement and analysis of Electrodermal Activity (EDA) offers appl...
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02/25/2015
On Convolutional Approximations to Linear Dimensionality Reduction Operators for Large Scale Data Processing
In this paper, we examine the problem of approximating a general linear ...
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11/02/2014
Noisy Matrix Completion under Sparse Factor Models
This paper examines a general class of noisy matrix completion tasks whe...
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07/01/2014
Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling
This paper examines the problem of locating outlier columns in a large, ...
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11/21/2013
Compressive Measurement Designs for Estimating Structured Signals in Structured Clutter: A Bayesian Experimental Design Approach
This work considers an estimation task in compressive sensing, where the...
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06/18/2013
On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements
Recent breakthrough results in compressive sensing (CS) have established...
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10/09/2012
Level Set Estimation from Compressive Measurements using Box Constrained Total Variation Regularization
Estimating the level set of a signal from measurements is a task that ar...
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11/29/2011
Efficient Adaptive Compressive Sensing Using Sparse Hierarchical Learned Dictionaries
Recent breakthrough results in compressed sensing (CS) have established ...
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01/29/2010
