23 citations · 57 across the 29 of their papers we have counts for
3 papers · 1 filter
Price of Fairness in Bandits: A Tight Minimax Characterization
Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray Chowdhury
In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as c…
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…
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…