Showing stat.MLShow all
3 papers · 1 filter
stat.ML2023
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 …
stat.ML2023
Structured Approximations of Measures
Keaton Hamm, Varun Khurana
We study the approximation of probability measures in the Wasserstein- distance by structured classes of approximators, motivated by applications in imaging, machine learning, a…
stat.ML2023
On Wasserstein distances for affine transformations of random vectors
Keaton Hamm, Andrzej Korzeniowski
We expound on some known lower bounds of the quadratic Wasserstein distance between random vectors in with an emphasis on affine transformations that have been used…