4 citations · 5 across the 8 of their papers we have counts for
8 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…
Efficient Algorithms For Fair Clustering with a New Fairness Notion
Shivam Gupta, Ganesh Ghalme, Narayanan C. Krishnan +1
We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every clust…
Sleeping Combinatorial Bandits
Kumar Abhishek, Ganesh Ghalme, Sujit Gujar +1
In this paper, we study an interesting combination of sleeping and combinatorial stochastic bandits. In the mixed model studied here, at each discrete time instant, an arbitrary \e…
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…
Ballooning Multi-Armed Bandits
Ganesh Ghalme, Swapnil Dhamal, Shweta Jain +2
In this paper, we introduce Ballooning Multi-Armed Bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (…