2 citations · 4 across the 5 of their papers we have counts for
5 papers
Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
Swetha Ganesh, Jiayu Chen, Gugan Thoppe +1
Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of th…
Online Learning with Adversaries: A Differential-Inclusion Analysis
Swetha Ganesh, Alexandre Reiffers-Masson, Gugan Thoppe
We introduce an observation-matrix-based framework for fully asynchronous online Federated Learning (FL) with adversaries. In this work, we demonstrate its effectiveness in estimat…
Multi Timescale Stochastic Approximation: Stability and Convergence
Rohan Deb, Swetha Ganesh, Shalabh Bhatnagar
This paper presents the first sufficient conditions that guarantee the stability and almost sure convergence of multi-timescale stochastic approximation (SA) iterates. It extends t…
Does Momentum Help? A Sample Complexity Analysis
Swetha Ganesh, Rohan Deb, Gugan Thoppe +1
Stochastic Heavy Ball (SHB) and Nesterov's Accelerated Stochastic Gradient (ASG) are popular momentum methods in stochastic optimization. While benefits of such acceleration ideas…
Trees with Matrix Weights: Laplacian Matrix and Characteristic-like Vertices
Swetha Ganesh, Sumit Mohanty
It is known that there is an alternative characterization of characteristic vertices for trees with positive weights on their edges via Perron values and Perron branches. Moreover,…