147 citations · 194 across the 25 of their papers we have counts for
6 papers · 1 filter
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference
Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury +1
We present a non-asymptotic lower bound on the eigenspectrum of the design matrix generated by any linear bandit algorithm with sub-linear regret when the action set has well-behav…
Distributed Newton Can Communicate Less and Resist Byzantine Workers
Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar
We develop a distributed second order optimization algorithm that is communication-efficient as well as robust against Byzantine failures of the worker machines. We propose COMRADE…
Communication-Efficient and Byzantine-Robust Distributed Learning with Error Feedback
Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe +2
We develop a communication-efficient distributed learning algorithm that is robust against Byzantine worker machines. We propose and analyze a distributed gradient-descent algorith…
Robust Federated Learning in a Heterogeneous Environment
Avishek Ghosh, Justin Hong, Dong Yin +1
We study a recently proposed large-scale distributed learning paradigm, namely Federated Learning, where the worker machines are end users' own devices. Statistical and computation…
Online Scoring with Delayed Information: A Convex Optimization Viewpoint
Avishek Ghosh, Kannan Ramchandran
We consider a system where agents enter in an online fashion and are evaluated based on their attributes or context vectors. There can be practical situations where this context is…
Misspecified Linear Bandits
Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan
We consider the problem of online learning in misspecified linear stochastic multi-armed bandit problems. Regret guarantees for state-of-the-art linear bandit algorithms such as Op…