19 citations · 36 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
Strategic Representation
Vineet Nair, Ganesh Ghalme, Inbal Talgam-Cohen +1
Humans have come to rely on machines for reducing excessive information to manageable representations. But this reliance can be abused -- strategic machines might craft representat…
A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounders
Aurghya Maiti, Vineet Nair, Gaurav Sinha
We study the problem of determining the best intervention in a Causal Bayesian Network (CBN) specified only by its causal graph. We model this as a stochastic multi-armed bandit (M…
Strategic Classification in the Dark
Ganesh Ghalme, Vineet Nair, Itay Eilat +2
Strategic classification studies the interaction between a classification rule and the strategic agents it governs. Under the assumption that the classifier is known, rational agen…
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…
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…