192 citations · 336 across the 18 of their papers we have counts for
5 papers · 1 filter
Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learning
Jackson A. Killian, Lily Xu, Arpita Biswas +1
We introduce robustness in \textit{restless multi-armed bandits} (RMABs), a popular model for constrained resource allocation among independent stochastic processes (arms). Nearly…
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…
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…
Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems
Aditya Mate, Arpita Biswas, Christoph Siebenbrunner +2
We propose Streaming Bandits, a Restless Multi Armed Bandit (RMAB) framework in which heterogeneous arms may arrive and leave the system after staying on for a finite lifetime. Str…
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…