A nonparametric estimator for Wasserstein barycenters achieves improved convergence rates by incorporating smoothness via density estimation and Sobolev geometry.
Mathematical Methods of Operations Research , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A mirror descent algorithm computes exact Wasserstein barycenters for mixed discrete and continuous input measures with convergence guarantees.
citing papers explorer
-
Smoothed estimation of Wasserstein barycenters
A nonparametric estimator for Wasserstein barycenters achieves improved convergence rates by incorporating smoothness via density estimation and Sobolev geometry.
-
A Unified Approach for Computing Wasserstein Barycenters of Discrete and Continuous Measures
A mirror descent algorithm computes exact Wasserstein barycenters for mixed discrete and continuous input measures with convergence guarantees.