53 citations · 293 across the 32 of their papers we have counts for
5 papers · 1 filter
Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring
Mojtaba Kadkhodaie Elyaderani, Swayambhoo Jain, Jeffrey Druce +2
This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \emph{group-sparse} stru…
Robust Low-Complexity Randomized Methods for Locating Outliers in Large Matrices
Xingguo Li, Jarvis Haupt
This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where {}{the data} are noisy, or where the overall matrix has missing…
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models
Abhinav V. Sambasivan, Jarvis D. Haupt
This paper examines fundamental error characteristics for a general class of matrix completion problems, where the matrix of interest is a product of two a priori unknown matrices,…
Identifying Outliers in Large Matrices via Randomized Adaptive Compressive Sampling
Xingguo Li, Jarvis Haupt
This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establi…
On the Fundamental Limits of Recovering Tree Sparse Vectors from Noisy Linear Measurements
Akshay Soni, Jarvis Haupt
Recent breakthrough results in compressive sensing (CS) have established that many high dimensional signals can be accurately recovered from a relatively small number of non-adapti…