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 • 17 Jun 2022 • Vineet Nair, Ganesh Ghalme, Inbal Talgam-Cohen, Nir Rosenfeld
In our main setting of interest, the system represents attributes of an item to the user, who then decides whether or not to consume.
no code implementations • 28 Apr 2022 • Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corinne Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe, Ayan Mukhopadhyay
More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake.
no code implementations • 6 Jul 2021 • Aurghya Maiti, Vineet Nair, Gaurav Sinha
First, we propose a simple regret minimization algorithm that takes as input a semi-Markovian causal graph with atomic interventions and possibly unobservable variables, and achieves $\tilde{O}(\sqrt{M/T})$ expected simple regret, where $M$ is dependent on the input CBN and could be very small compared to the number of arms.
1 code implementation • 23 Feb 2021 • Ganesh Ghalme, Vineet Nair, Itay Eilat, Inbal Talgam-Cohen, Nir Rosenfeld
Strategic classification studies the interaction between a classification rule and the strategic agents it governs.
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 • 17 Jul 2020 • Nir Ailon, Omer Leibovich, Vineet Nair
Motivated by these facts, we propose to replace a dense linear layer in any neural network by an architecture based on the butterfly network.
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.