35 citations · 107 across the 26 of their papers we have counts for
6 papers · 1 filter
On the Benefits of Inducing Local Lipschitzness for Robust Generative Adversarial Imitation Learning
Farzan Memarian, Abolfazl Hashemi, Scott Niekum +1
We explore methodologies to improve the robustness of generative adversarial imitation learning (GAIL) algorithms to observation noise. Towards this objective, we study the effect…
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
Anish Acharya, Abolfazl Hashemi, Prateek Jain +3
Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakd…
On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updates
Rudrajit Das, Abolfazl Hashemi, Sujay Sanghavi +1
There is a dearth of convergence results for differentially private federated learning (FL) with non-Lipschitz objective functions (i.e., when gradient norms are not bounded). The…
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…
Physical-Layer Security via Distributed Beamforming in the Presence of Adversaries with Unknown Locations
Yagiz Savas, Abolfazl Hashemi, Abraham P. Vinod +2
We study the problem of securely communicating a sequence of information bits with a client in the presence of multiple adversaries at unknown locations in the environment. We assu…
Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs
Yiyue Chen, Abolfazl Hashemi, Haris Vikalo
We consider the problem of decentralized optimization over time-varying directed networks. The network nodes can access only their local objectives, and aim to collaboratively mini…