5 papers · 1 filter
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…
Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits
Siddhartha Banerjee, Sean R. Sinclair, Milind Tambe +2
Most real-world deployments of bandit algorithms exist somewhere in between the offline and online set-up, where some historical data is available upfront and additional data is co…
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…
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…
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…