61 citations · 167 across the 16 of their papers we have counts for
16 papers
Sparse PCA: Phase Transitions in the Critical Regime
Michael J. Feldman, Theodor Misiakiewicz, Elad Romanov
This work studies estimation of sparse principal components in high dimensions. Specifically, we consider a class of estimators based on kernel PCA, generalizing the covariance thr…
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing
Elad Romanov, Fangzhao Zhang, Mert Pilanci
Motivated by recent advances in serverless cloud computing, in particular the "function as a service" (FaaS) model, we consider the problem of minimizing a convex function in a mas…
The High-Dimensional Asymptotics of Principal Component Regression
Alden Green, Elad Romanov
We study principal components regression (PCR) in an asymptotic high-dimensional regression setting, where the number of data points is proportional to the dimension. We derive exa…
On the Noise Sensitivity of the Randomized SVD
Elad Romanov
The randomized singular value decomposition (R-SVD) is a popular sketching-based algorithm for efficiently computing the partial SVD of a large matrix. When the matrix is low-rank,…
Matrix Denoising with Partial Noise Statistics: Optimal Singular Value Shrinkage of Spiked F-Matrices
Matan Gavish, William Leeb, Elad Romanov
We study the problem of estimating a large, low-rank matrix corrupted by additive noise of unknown covariance, assuming one has access to additional side information in the form of…
Tyler's and Maronna's M-estimators: Non-Asymptotic Concentration Results
Elad Romanov, Gil Kur, Boaz Nadler
Tyler's and Maronna's M-estimators, as well as their regularized variants, are popular robust methods to estimate the scatter or covariance matrix of a multivariate distribution. I…
On the Role of Channel Capacity in Learning Gaussian Mixture Models
Elad Romanov, Tamir Bendory, Or Ordentlich
This paper studies the sample complexity of learning the unknown centers of a balanced Gaussian mixture model (GMM) in with spherical covariance matrix $σ^2\math…
Spiked Covariance Estimation from Modulo-Reduced Measurements
Elad Romanov, Or Ordentlich
Consider the rank-1 spiked model: , where is the spike intensity, is an unknown direction and $ξ\sim \mathcal{N}(0,1),…
On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
Elad Romanov, Or Ordentlich
Motivated by applications in unsourced random access, this paper develops a novel scheme for the problem of compressed sensing of binary signals. In this problem, the goal is to de…
ScreeNOT: Exact MSE-Optimal Singular Value Thresholding in Correlated Noise
David L. Donoho, Matan Gavish, Elad Romanov
We derive a formula for optimal hard thresholding of the singular value decomposition in the presence of correlated additive noise; although it nominally involves unobservables, we…
Multi-reference alignment in high dimensions: sample complexity and phase transition
Elad Romanov, Tamir Bendory, Or Ordentlich
Multi-reference alignment entails estimating a signal in from its circularly-shifted and noisy copies. This problem has been studied thoroughly in recent years, focu…
Above the Nyquist Rate, Modulo Folding Does Not Hurt
Elad Romanov, Or Ordentlich
We consider the problem of recovering a continuous-time bandlimited signal from the discrete-time signal obtained from sampling it every seconds and reducing the result modul…
Blind Unwrapping of Modulo Reduced Gaussian Vectors: Recovering MSBs from LSBs
Elad Romanov, Or Ordentlich
We consider the problem of recovering i.i.d samples from a zero mean multivariate Gaussian distribution with an unknown covariance matrix, from their modulo wrapped measurement…
Optimal spectral shrinkage and PCA with heteroscedastic noise
William Leeb, Elad Romanov
This paper studies the related problems of prediction, covariance estimation, and principal component analysis for the spiked covariance model with heteroscedastic noise. We consid…
The Noise-Sensitivity Phase Transition in Spectral Group Synchronization Over Compact Groups
Elad Romanov, Matan Gavish
In Group Synchronization, one attempts to find a collection of unknown group elements from noisy measurements of their pairwise differences. Several important problems in vision an…
Near-optimal matrix recovery from random linear measurements
Elad Romanov, Matan Gavish
In matrix recovery from random linear measurements, one is interested in recovering an unknown -by- matrix from measurements where each …