336 citations · 1.1k across the 77 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…
Sinkhorn Natural Gradient for Generative Models
Zebang Shen, Zhenfu Wang, Alejandro Ribeiro +1
We consider the problem of minimizing a functional over a parametric family of probability measures, where the parameterization is characterized via a push-forward structure. An im…
Sinkhorn Barycenter via Functional Gradient Descent
Zebang Shen, Zhenfu Wang, Alejandro Ribeiro +1
In this paper, we consider the problem of computing the barycenter of a set of probability distributions under the Sinkhorn divergence. This problem has recently found applications…
Submodular Meta-Learning
Arman Adibi, Aryan Mokhtari, Hamed Hassani
In this paper, we introduce a discrete variant of the meta-learning framework. Meta-learning aims at exploiting prior experience and data to improve performance on future tasks. By…
Safe Learning under Uncertain Objectives and Constraints
Mohammad Fereydounian, Zebang Shen, Aryan Mokhtari +2
In this paper, we consider non-convex optimization problems under \textit{unknown} yet safety-critical constraints. Such problems naturally arise in a variety of domains including…
Learning to Track Dynamic Targets in Partially Known Environments
Heejin Jeong, Hamed Hassani, Manfred Morari +2
We solve active target tracking, one of the essential tasks in autonomous systems, using a deep reinforcement learning (RL) approach. In this problem, an autonomous agent is tasked…
Provable tradeoffs in adversarially robust classification
Edgar Dobriban, Hamed Hassani, David Hong +1
It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open…
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Alexander Robey, Hamed Hassani, George J. Pappas
While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperc…
Precise Tradeoffs in Adversarial Training for Linear Regression
Adel Javanmard, Mahdi Soltanolkotabi, Hamed Hassani
Despite breakthrough performance, modern learning models are known to be highly vulnerable to small adversarial perturbations in their inputs. While a wide variety of recent \emph{…
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…