8 citations · 27 across the 9 of their papers we have counts for
16 papers
Online Stochastic Gradient Descent Learns Linear Dynamical Systems from A Single Trajectory
Navid Reyhanian, Jarvis Haupt
This work investigates the problem of estimating the weight matrices of a stable time-invariant linear dynamical system from a single sequence of noisy measurements. We show that i…
Convexifying Sparse Interpolation with Infinitely Wide Neural Networks: An Atomic Norm Approach
Akshay Kumar, Jarvis Haupt
This work examines the problem of exact data interpolation via sparse (neuron count), infinitely wide, single hidden layer neural networks with leaky rectified linear unit activati…
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We consider the problem of factorizing a structured 3-way tensor into its constituent Canonical Polyadic (CP) factors. This decomposition, which can be viewed as a generalization o…
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems
Jineng Ren, Jarvis Haupt
This paper proposes and analyzes a communication-efficient distributed optimization framework for general nonconvex nonsmooth signal processing and machine learning problems under…
Target-based Hyperspectral Demixing via Generalized Robust PCA
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Localizing targets of interest in a given hyperspectral (HS) image has applications ranging from remote sensing to surveillance. This task of target detection leverages the fact th…
A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing
Sirisha Rambhatla, Xingguo Li, Jineng Ren +1
We consider the task of localizing targets of interest in a hyperspectral (HS) image based on their spectral signature(s), by posing the problem as two distinct convex demixing tas…