arXiv Machine Learning By Egor Gladin, Alexey Kroshnin, Jia-Jie Zhu, Pavel Dvurechensky

Improved Stochastic Optimization of LogSumExp

Read the original on arXiv Machine Learning →

arXiv:2509. 24894v4 Announce Type: replace-cross Abstract: The LogSumExp function, dual to the Kullback-Leibler (KL) divergence, plays a central role in many important optimization problems, including entropy-regularized optimal transport (OT) and distributionally robust optimization (DRO).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 5

Variational Entropic Optimal Transport

arXiv:2602. 02241v2 Announce Type: replace Abstract: Entropic optimal transport (EOT) in continuous spaces with quadratic cost is a classical tool for solving the domain translation problem.

By Roman Dyachenko, Nikita Gushchin, Kirill Sokolov, Petr Mokrov, Evgeny Burnaev, Alexander Korotin
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