12 citations · 46 across the 17 of their papers we have counts for
16 papers · 1 filter
Reinforcement learning with combinatorial actions for coupled restless bandits
Lily Xu, Bryan Wilder, Elias B. Khalil +1
Reinforcement learning (RL) has increasingly been applied to solve real-world planning problems, with progress in handling large state spaces and time horizons. However, a key bott…
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang +2
Comparing datasets is a fundamental task in machine learning, essential for various learning paradigms-from evaluating train and test datasets for model generalization to using dat…
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…
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…