arXiv Machine Learning
Jun 25

Sample complexity of unbalanced entropic OT

arXiv:2606. 24987v1 Announce Type: cross Abstract: Optimal transport (OT) has become a central language for comparing probability measures, but exact balanced OT is often both too rigid for data with missing, created, or destroyed mass and subject to unfavorable high-dimensional sample complexity.

By Francisco Andrade, Gabriel Peyr\'e, Clarice Poon
arXiv Machine Learning
Jun 30

Learning from samples: inverse problems over measures

arXiv:2505. 07124v3 Announce Type: replace Abstract: We study inverse problems where an unknown potential is observed only through samples from the measure it induces by a convex variational principle.

By Francisco Andrade, Gabriel Peyr\'e, Clarice Poon
arXiv Machine Learning
Jun 17

Constrained Diffusion Models with Primal-Dual Inference

arXiv:2606. 17192v1 Announce Type: new Abstract: This paper develops constrained diffusion models with primal-dual inference (PDI) to sample from optimal distributions of entropy-regularized optimization problems with \emph{average} constraints.

By Samar Hadou, Yigit Berkay Uslu, Alejandro Ribeiro