Full Professor, Harvard University
2 papers at NeurIPS 2025
We propose a flow matching method for reinforcement learning with shifted-dynamics data.
We develop a policy for adaptive exploration on a graph under frontier constraint that is optimal for trees based on Gittins index, and show how we can apply it to network-based testing in public health settings