2 papers
stat.ML2026
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.ML2025
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 $\mathcal{P}_{\mathrm{a.c.}}(Ω)…