19 citations · 25 across the 6 of their papers we have counts for
6 papers
Mitigating Disparity while Maximizing Reward: Tight Anytime Guarantee for Improving Bandits
Vishakha Patil, Vineet Nair, Ganesh Ghalme +1
We study the Improving Multi-Armed Bandit (IMAB) problem, where the reward obtained from an arm increases with the number of pulls it receives. This model provides an elegant abstr…
Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment
Ganesh Ghalme, Vineet Nair, Vishakha Patil +1
Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare. In this work, we study fairness in…
Budgeted and Non-budgeted Causal Bandits
Vineet Nair, Vishakha Patil, Gaurav Sinha
Learning good interventions in a causal graph can be modelled as a stochastic multi-armed bandit problem with side-information. First, we study this problem when interventions are…
Streaming Algorithms for Stochastic Multi-armed Bandits
Arnab Maiti, Vishakha Patil, Arindam Khan
We study the Stochastic Multi-armed Bandit problem under bounded arm-memory. In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory a…
Achieving Fairness in the Stochastic Multi-armed Bandit Problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair +1
We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of…
Achieving Fairness in Stochastic Multi-armed Bandit Problem
Vishakha Patil, Ganesh Ghalme, Vineet Nair +1
We study an interesting variant of the stochastic multi-armed bandit problem, called the Fair-SMAB problem, where each arm is required to be pulled for at least a given fraction of…