4 papers
A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers
Roman Tarasov, Petr Mokrov, Milena Gazdieva +2
Neural network-based optimal transport (OT) is a recent and fruitful direction in the generative modeling community. It finds its applications in various fields such as domain tran…
Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport
Milena Gazdieva, Jaemoo Choi, Alexander Kolesov +3
Aggregating data from multiple sources can be formalized as an Optimal Transport (OT) barycenter problem, which seeks to compute the average of probability distributions with respe…
Estimating Barycenters of Distributions with Neural Optimal Transport
Alexander Kolesov, Petr Mokrov, Igor Udovichenko +4
Given a collection of probability measures, a practitioner sometimes needs to find an "average" distribution which adequately aggregates reference distributions. A theoretically ap…
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Alexander Kolesov, Petr Mokrov, Igor Udovichenko +5
Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter tas…