35 citations · 107 across the 26 of their papers we have counts for
6 papers · 1 filter
Generalization Bounds for Sparse Random Feature Expansions
Abolfazl Hashemi, Hayden Schaeffer, Robert Shi +3
Random feature methods have been successful in various machine learning tasks, are easy to compute, and come with theoretical accuracy bounds. They serve as an alternative approach…
Faster Non-Convex Federated Learning via Global and Local Momentum
Rudrajit Das, Anish Acharya, Abolfazl Hashemi +3
We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of to converge to an -stationary point (i.e., $\math…
Sampling and Reconstruction of Graph Signals via Weak Submodularity and Semidefinite Relaxation
Abolfazl Hashemi, Rasoul Shafipour, Haris Vikalo +1
We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the si…
Sparse recovery via Orthogonal Least-Squares under presence of Noise
Abolfazl Hashemi, Haris Vikalo
We consider the Orthogonal Least-Squares (OLS) algorithm for the recovery of a -dimensional -sparse signal from a low number of noisy linear measurements. The Exact Recovery…
Sampling Requirements and Accelerated Schemes for Sparse Linear Regression with Orthogonal Least-Squares
Abolfazl Hashemi, Haris Vikalo
We study the problem of inferring a sparse vector from random linear combinations of its components. We propose the Accelerated Orthogonal Least-Squares (AOLS) algorithm that impro…
Sparse Linear Regression via Generalized Orthogonal Least-Squares
Abolfazl Hashemi, Haris Vikalo
Sparse linear regression, which entails finding a sparse solution to an underdetermined system of linear equations, can formally be expressed as an -constrained least-squares…