activity
20172022
most citedUniversality in Learning from Linear Measurements

9 citations · 15 across the 6 of their papers we have counts for

collaborators

9 papers

cs.LG2022★ 1 cited

Stochastic Mirror Descent in Average Ensemble Models

Taylan Kargin, Fariborz Salehi, Babak Hassibi

The stochastic mirror descent (SMD) algorithm is a general class of training algorithms, which includes the celebrated stochastic gradient descent (SGD), as a special case. It util…

cs.LG2020

Robustifying Binary Classification to Adversarial Perturbation

Fariborz Salehi, Babak Hassibi

Despite the enormous success of machine learning models in various applications, most of these models lack resilience to (even small) perturbations in their input data. Hence, new…

stat.ML2020★ 2 cited

The Performance Analysis of Generalized Margin Maximizer (GMM) on Separable Data

Fariborz Salehi, Ehsan Abbasi, Babak Hassibi

Logistic models are commonly used for binary classification tasks. The success of such models has often been attributed to their connection to maximum-likelihood estimators. It has…

cs.DC2019

Federated Learning with Autotuned Communication-Efficient Secure Aggregation

Keith Bonawitz, Fariborz Salehi, Jakub Konečný +2

Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do mac…

math.ST2019★ 9 cited

Universality in Learning from Linear Measurements

Ehsan Abbasi, Fariborz Salehi, Babak Hassibi

We study the problem of recovering a structured signal from independently and identically drawn linear measurements. A convex penalty function is considered which penali…

stat.ML2019

The Impact of Regularization on High-dimensional Logistic Regression

Fariborz Salehi, Ehsan Abbasi, Babak Hassibi

Logistic regression is commonly used for modeling dichotomous outcomes. In the classical setting, where the number of observations is much larger than the number of parameters, pro…