1 citations · 2 across the 5 of their papers we have counts for
Showing 2023Show all
2 papers · 1 filter
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 …
cs.LG2023
Linearized Wasserstein dimensionality reduction with approximation guarantees
Alexander Cloninger, Keaton Hamm, Varun Khurana +1
We introduce LOT Wassmap, a computationally feasible algorithm to uncover low-dimensional structures in the Wasserstein space. The algorithm is motivated by the observation that ma…