147 citations · 194 across the 25 of their papers we have counts for
10 papers · 1 filter
Model Selection in Reinforcement Learning with General Function Approximations
Avishek Ghosh, Sayak Ray Chowdhury
We consider model selection for classic Reinforcement Learning (RL) environments -- Multi Armed Bandits (MABs) and Markov Decision Processes (MDPs) -- under general function approx…
Decentralized Competing Bandits in Non-Stationary Matching Markets
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran +2
Understanding complex dynamics of two-sided online matching markets, where the demand-side agents compete to match with the supply-side (arms), has recently received substantial in…
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
Avishek Ghosh, Arya Mazumdar, Soumyabrata Pal +1
While mixture of linear regressions (MLR) is a well-studied topic, prior works usually do not analyze such models for prediction error. In fact, {\em prediction} and {\em loss} are…
Breaking the Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits
Avishek Ghosh, Abishek Sankararaman
We prove an instance independent (poly) logarithmic regret for stochastic contextual bandits with linear payoff. Previously, in \cite{chu2011contextual}, a lower bound of $\mathcal…
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…
Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran
We consider the problem of model selection for two popular stochastic linear bandit settings, and propose algorithms that adapts to the unknown problem complexity. In the first set…
An Efficient Framework for Clustered Federated Learning
Avishek Ghosh, Jichan Chung, Dong Yin +1
We address the problem of federated learning (FL) where users are distributed and partitioned into clusters. This setup captures settings where different groups of users have their…
Alternating Minimization Converges Super-Linearly for Mixed Linear Regression
Avishek Ghosh, Kannan Ramchandran
We address the problem of solving mixed random linear equations. We have unlabeled observations coming from multiple linear regressions, and each observation corresponds to exactly…
Max-Affine Regression: Provable, Tractable, and Near-Optimal Statistical Estimation
Avishek Ghosh, Ashwin Pananjady, Adityanand Guntuboyina +1
Max-affine regression refers to a model where the unknown regression function is modeled as a maximum of unknown affine functions for a fixed . This generalizes linea…