SIAM Journal on Mathematics of Data Science

Authors Staudt T, Hundrieser S, Munk A Journal arXiv Citation arXiv:2201.08316. Abstract Kantorovich potentials denote the dual solutions of the renowned optimal transportation problem. Uniqueness of these solutions is relevant from both a theoretical and an algorithmic point of view, and has recently emerged as a necessary condition for asymptotic

Authors Mordant G, Munk A Journal SIAM Journal on Mathematics of Data Science Citation IAM Journal on Mathematics of Data Science 2023 5:2, 528-557 Abstract In this paper, we consider a certain convolutional Laplacian for metric measure spaces and investigate its potential for the statistical analysis of complex objects. The

Authors Behr M, Munk A Journal SIAM Journal on Mathematics of Data Science Citation SIAM Journal on Mathematics of Data Science 4(2):490-513. Abstract We provide a minimax optimal estimation procedure for F and W in matrix valued linear models Y = F W + Z where the parameter matrix W

Authors Heinemann F, Munk A, Zemel Y Journal SIAM Journal on Mathematics of Data Science Citation SIAM Journal on Mathematics of Data Science 2022 4:1, 229-259. Abstract We propose a hybrid resampling method to approximate finitely supported Wasserstein barycenters on large-scale datasets, which can be combined with any exact solver.

Authors Klatt M, Tameling C, Munk A Journal SIAM Journal on Mathematics of Data Science Citation SIAM Journal on Mathematics of Data Science 2 (2), 419-443. Abstract We derive limit distributions for various empirical regularized optimal transport quantities between probability distributions supported on a finite metric space and show their