@inproceedings{
assel2023optimal,
title={Optimal Transport with Adaptive Regularisation},
author={Hugues Van Assel and Titouan Vayer and R{\'e}mi Flamary and Nicolas Courty},
booktitle={NeurIPS 2023 Workshop Optimal Transport and Machine Learning},
year={2023},
url={https://openreview.net/forum?id=Kwrz4fYD2E}
}
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan.
Many formulations impose a global constraint on the transport plan, for instance by relying on entropic regularisation. 
As it is more expensive to diffuse mass for outlier points compared to central ones, this typically results in a significant imbalance in the way mass is spread across the points.
This can be detrimental for some applications where a minimum of smoothing is required per point.  
To remedy this, we introduce OT with Adaptive RegularIsation (OTARI), a new formulation of OT that imposes constraints on the mass going in or/and out of each point.
We then showcase the benefits of this approach for domain adaptation.
Optimal Transport with Adaptive Regularisation
Hugues Van Assel Titouan Vayer Rémi Flamary Nicolas Courty