12 citations · 64 across the 36 of their papers we have counts for
9 papers · 1 filter
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping
Lauren H. Cooke, Harvey Klyne, Edwin Zhang +3
Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work m…
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Yunfan Zhao, Nikhil Behari, Edward Hughes +5
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently…
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…
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
Lily Xu, Esther Rolf, Sara Beery +21
In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Res…
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…
Leaving the Nest: Going Beyond Local Loss Functions for Predict-Then-Optimize
Sanket Shah, Andrew Perrault, Bryan Wilder +1
Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty. The central research question it asks is, "How can the structure of a…