no code implementations • 19 Aug 2022 • Vishakha Patil, Vineet Nair, Ganesh Ghalme, Arindam Khan
We study the tension that arises between two seemingly conflicting objectives in the horizon-unaware setting: a) maximizing the cumulative reward at any time based on current rewards of the arms, and b) ensuring that arms with better long-term rewards get sufficient opportunities even if they initially have low rewards.
no code implementations • NeurIPS 2021 • Arnab Maiti, Vishakha Patil, Arindam Khan
In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory at any time, is bounded.
no code implementations • 14 Feb 2021 • Ganesh Ghalme, Vineet Nair, Vishakha Patil, Yilun Zhou
Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare.
no code implementations • 13 Dec 2020 • Vineet Nair, Vishakha Patil, Gaurav Sinha
If there are no backdoor paths from an intervenable node to the reward node then we propose an algorithm to minimize simple regret that optimally trades-off observations and interventions based on the cost of intervention.
no code implementations • 9 Dec 2020 • Arnab Maiti, Vishakha Patil, Arindam Khan
In this setting, the arms arrive in a stream, and the number of arms that can be stored in the memory at any time, is bounded.
no code implementations • 23 Jul 2019 • Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.
no code implementations • 27 May 2019 • Vishakha Patil, Ganesh Ghalme, Vineet Nair, Y. Narahari
Finally, we evaluate the cost of fairness in terms of the conventional notion of regret.