4 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents
Arman Adibi, Aritra Mitra, George J. Pappas +1
Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control…
Linear Stochastic Bandits over a Bit-Constrained Channel
Aritra Mitra, Hamed Hassani, George J. Pappas
One of the primary challenges in large-scale distributed learning stems from stringent communication constraints. While several recent works address this challenge for static optim…
Exploiting Heterogeneity in Robust Federated Best-Arm Identification
Aritra Mitra, Hamed Hassani, George Pappas
We study a federated variant of the best-arm identification problem in stochastic multi-armed bandits: a set of clients, each of whom can sample only a subset of the arms, collabor…
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
Aritra Mitra, Rayana Jaafar, George J. Pappas +1
We consider a standard federated learning (FL) architecture where a group of clients periodically coordinate with a central server to train a statistical model. We develop a genera…
Near-Optimal Data Source Selection for Bayesian Learning
Lintao Ye, Aritra Mitra, Shreyas Sundaram
We study a fundamental problem in Bayesian learning, where the goal is to select a set of data sources with minimum cost while achieving a certain learning performance based on the…