147 citations · 194 across the 25 of their papers we have counts for
4 papers · 1 filter
Model Selection for Generic Reinforcement Learning
Avishek Ghosh, Sayak Ray Chowdhury, Kannan Ramchandran
We address the problem of model selection for the finite horizon episodic Reinforcement Learning (RL) problem where the transition kernel belongs to a family of models $\math…
Model Selection for Generic Contextual Bandits
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran
We consider the problem of model selection for the general stochastic contextual bandits under the realizability assumption. We propose a successive refinement based algorithm call…
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
Vipul Gupta, Avishek Ghosh, Michal Derezinski +3
To address the communication bottleneck problem in distributed optimization within a master-worker framework, we propose LocalNewton, a distributed second-order algorithm with loca…
Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience
Avishek Ghosh, Raj Kumar Maity, Arya Mazumdar +1
The problem of saddle-point avoidance for non-convex optimization is quite challenging in large scale distributed learning frameworks, such as Federated Learning, especially in the…