110 citations · 173 across the 9 of their papers we have counts for
10 papers
Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats
Lucia Gordon, Nikhil Behari, Samuel Collier +6
Much of Earth's charismatic megafauna is endangered by human activities, particularly the rhino, which is at risk of extinction due to the poaching crisis in Africa. Monitoring rhi…
Equitable Restless Multi-Armed Bandits: A General Framework Inspired By Digital Health
Jackson A. Killian, Manish Jain, Yugang Jia +3
Restless multi-armed bandits (RMABs) are a popular framework for algorithmic decision making in sequential settings with limited resources. RMABs are increasingly being used for se…
Limited Resource Allocation in a Non-Markovian World: The Case of Maternal and Child Healthcare
Panayiotis Danassis, Shresth Verma, Jackson A. Killian +2
The success of many healthcare programs depends on participants' adherence. We consider the problem of scheduling interventions in low resource settings (e.g., placing timely suppo…
Fairness for Workers Who Pull the Arms: An Index Based Policy for Allocation of Restless Bandit Tasks
Arpita Biswas, Jackson A. Killian, Paula Rodriguez Diaz +2
Motivated by applications such as machine repair, project monitoring, and anti-poaching patrol scheduling, we study intervention planning of stochastic processes under resource con…
Networked Restless Multi-Armed Bandits for Mobile Interventions
Han-Ching Ou, Christoph Siebenbrunner, Jackson Killian +4
Motivated by a broad class of mobile intervention problems, we propose and study restless multi-armed bandits (RMABs) with network effects. In our model, arms are partially recharg…
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…
Envisioning Communities: A Participatory Approach Towards AI for Social Good
Elizabeth Bondi, Lily Xu, Diana Acosta-Navas +1
Research in artificial intelligence (AI) for social good presupposes some definition of social good, but potential definitions have been seldom suggested and never agreed upon. The…
Collapsing Bandits and Their Application to Public Health Interventions
Aditya Mate, Jackson A. Killian, Haifeng Xu +2
We propose and study Collpasing Bandits, a new restless multi-armed bandit (RMAB) setting in which each arm follows a binary-state Markovian process with a special structure: when…
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
Jackson A. Killian, Bryan Wilder, Amit Sharma +4
Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phon…