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stat.ML2026
Optimal Transport Meets Reinforcement Learning: A Survey
Yujie Zhu, Charles A. Hepburn, Matthew Thorpe +1
Reinforcement learning (RL) algorithms frequently compare probability distributions, such as state visitation distributions induced by policies and experts, action distributions fr…
stat.ML2026
Large Data Limits of Laplace Learning for Gaussian Measure Data in Infinite Dimensions
Zhengang Zhong, Yury Korolev, Matthew Thorpe
Laplace learning is a semi-supervised method, a solution for finding missing labels from a partially labeled dataset utilizing the geometry given by the unlabeled data points. The…
stat.ML2023★ 1 cited
Manifold learning in Wasserstein space
Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer +1
This paper aims at building the theoretical foundations for manifold learning algorithms in the space of absolutely continuous probability measures …