
Online Stochastic Gradient Descent Learns Linear Dynamical Systems from A Single Trajectory
This work investigates the problem of estimating the weight matrices of ...
read it

Convexifying Sparse Interpolation with Infinitely Wide Neural Networks: An Atomic Norm Approach
This work examines the problem of exact data interpolation via sparse (n...
read it

Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
We consider the problem of factorizing a structured 3way tensor into it...
read it

A Provably CommunicationEfficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems
This paper proposes and analyzes a communicationefficient distributed o...
read it

NOODL: Provable Online Dictionary Learning and Sparse Coding
We consider the dictionary learning problem, where the aim is to model t...
read it

TensorMap: LidarBased Topological Mapping and Localization via Tensor Decompositions
We propose a technique to develop (and localize in) topological maps fro...
read it

Targetbased Hyperspectral Demixing via Generalized Robust PCA
Localizing targets of interest in a given hyperspectral (HS) image has a...
read it

A DictionaryBased Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing
We consider the task of localizing targets of interest in a hyperspectra...
read it

A DictionaryBased Generalization of Robust PCA Part I: Study of Theoretical Properties
We consider the decomposition of a data matrix assumed to be a superposi...
read it

A Dictionary Based Generalization of Robust PCA
We analyze the decomposition of a data matrix, assumed to be a superposi...
read it

On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
Our paper proposes a generalization error bound for a general family of ...
read it

On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
We study constrained nonconvex optimization problems in machine learning...
read it

Near Optimal Sketching of LowRank Tensor Regression
We study the least squares regression problem _Θ∈S_ D,RAΘb_2, where S_...
read it

Communicationefficient Algorithm for Distributed Sparse Learning via Twoway Truncation
We propose a communicationally and computationally efficient algorithm f...
read it

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...
read it

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...
read it

Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor
In this paper we study the problem of noisy tensor completion for tensor...
read it

Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization
We propose a general theory for studying the geometry of nonconvex objec...
read it

A First Order Free Lunch for SQRTLasso
Many statistical machine learning techniques sacrifice convenient comput...
read it

Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
We propose a stochastic variance reduced optimization algorithm for solv...
read it

A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
The measurement and analysis of Electrodermal Activity (EDA) offers appl...
read it

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 ...
read it

Noisy Matrix Completion under Sparse Factor Models
This paper examines a general class of noisy matrix completion tasks whe...
read it

Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling
This paper examines the problem of locating outlier columns in a large, ...
read it

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...
read it

On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements
Recent breakthrough results in compressive sensing (CS) have established...
read it

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...
read it

Efficient Adaptive Compressive Sensing Using Sparse Hierarchical Learned Dictionaries
Recent breakthrough results in compressed sensing (CS) have established ...
read it

Distilled Sensing: Adaptive Sampling for Sparse Detection and Estimation
Adaptive sampling results in dramatic improvements in the recovery of sp...
read it