885 citations · 1.7k across the 71 of their papers we have counts for
10 papers · 1 filter
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity
Amirhossein Reisizadeh, Isidoros Tziotis, Hamed Hassani +2
Federated Learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local. It is, however, known th…
Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID-19 Pandemic
Mark Beliaev, Erdem Bıyık, Daniel A. Lazar +3
The COVID-19 pandemic has severely affected many aspects of people's daily lives. While many countries are in a re-opening stage, some effects of the pandemic on people's behaviors…
Adversarially Robust Classification based on GLRT
Bhagyashree Puranik, Upamanyu Madhow, Ramtin Pedarsani
Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore…
Asymptotic Behavior of Adversarial Training in Binary Classification
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
It has been consistently reported that many machine learning models are susceptible to adversarial attacks i.e., small additive adversarial perturbations applied to data points can…
Optimal Tolling for Multitype Mixed Autonomous Traffic Networks
Daniel A. Lazar, Ramtin Pedarsani
When selfish users share a road network and minimize their individual travel costs, the equilibrium they reach can be worse than the socially optimal routing. Tolls are often used…
Fundamental Limits of Ridge-Regularized Empirical Risk Minimization in High Dimensions
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
Empirical Risk Minimization (ERM) algorithms are widely used in a variety of estimation and prediction tasks in signal-processing and machine learning applications. Despite their p…
Robust Federated Learning: The Case of Affine Distribution Shifts
Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani +1
Federated learning is a distributed paradigm that aims at training models using samples distributed across multiple users in a network while keeping the samples on users' devices w…
Sharp Asymptotics and Optimal Performance for Inference in Binary Models
Hossein Taheri, Ramtin Pedarsani, Christos Thrampoulidis
We study convex empirical risk minimization for high-dimensional inference in binary models. Our first result sharply predicts the statistical performance of such estimators in the…
Polarizing Front Ends for Robust CNNs
Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic +2
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up str…
Quantized Decentralized Stochastic Learning over Directed Graphs
Hossein Taheri, Aryan Mokhtari, Hamed Hassani +1
We consider a decentralized stochastic learning problem where data points are distributed among computing nodes communicating over a directed graph. As the model size gets large, d…