4 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 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…