activity
20162022
most citedQ-Learning Lagrange Policies for Multi-Action Restless Bandits

12 citations · 38 across the 5 of their papers we have counts for

collaborators

10 papers

cs.AI2022

Ranked Prioritization of Groups in Combinatorial Bandit Allocation

Lily Xu, Arpita Biswas, Fei Fang +1

Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have…

cs.LG202112 cited

Q-Learning Lagrange Policies for Multi-Action Restless Bandits

Jackson A. Killian, Arpita Biswas, Sanket Shah +1

Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which independent processes are managed. However, previous wor…

cs.LG2021

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare

Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham +1

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may g…

cs.LG2020

Ensuring Fairness under Prior Probability Shifts

Arpita Biswas, Suvam Mukherjee

In this paper, we study the problem of fair classification in the presence of prior probability shifts, where the training set distribution differs from the test set. This phenomen…

cs.AI20204 cited

COVID-19: Strategies for Allocation of Test Kits

Arpita Biswas, Shruthi Bannur, Prateek Jain +1

With the increasing spread of COVID-19, it is important to systematically test more and more people. The current strategy for test-kit allocation is mostly rule-based, focusing on…

cs.AI2019

Quantifying Infra-Marginality and Its Trade-off with Group Fairness

Arpita Biswas, Siddharth Barman, Amit Deshpande +1

In critical decision-making scenarios, optimizing accuracy can lead to a biased classifier, hence past work recommends enforcing group-based fairness metrics in addition to maximiz…