@inproceedings{
kolesov2024energyguided,
title={Energy-Guided Continuous Entropic Barycenter Estimation for General Costs},
author={Alexander Kolesov and Petr Mokrov and Igor Udovichenko and Milena Gazdieva and Gudmund Pammer and Anastasis Kratsios and Evgeny Burnaev and Alexander Korotin},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=JZHFRLoqDq}
}
Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter task is to take the average of a collection of probability distributions w.r.t. given OT discrepancies. We propose a novel algorithm for approximating the continuous Entropic OT (EOT) barycenter for arbitrary OT cost functions. Our approach is built upon the dual reformulation of the EOT problem based on weak OT, which has recently gained the attention of the ML community. Beyond its novelty, our method enjoys several advantageous properties: (i) we establish quality bounds for the recovered solution; (ii) this approach seamlessly interconnects with the Energy-Based Models (EBMs) learning procedure enabling the use of well-tuned algorithms for the problem of interest; (iii) it provides an intuitive optimization scheme avoiding min-max, reinforce and other intricate technical tricks. For validation, we consider several low-dimensional scenarios and image-space setups, including *non-Euclidean* cost functions. Furthermore, we investigate the practical task of learning the barycenter on an image manifold generated by a pretrained generative model, opening up new directions for real-world applications. Our code is available at https://github.com/justkolesov/EnergyGuidedBarycenters.
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Alexander Kolesov Petr Mokrov Igor Udovichenko Milena Gazdieva Gudmund Pammer Anastasis Kratsios Evgeny Burnaev Alexander Korotin