← Back

RESEARCH

Optimal Transport

Optimal Transport theory provides a rigorous mathematical framework for comparing probability distributions. Originating in the work of Gaspard Monge and later broadened by Leonid Kantorovich, it offers a principled way to address questions of comparison, alignment, and transformation between measures.

In my research, it serves as a geometric foundation for modeling, computation, and theoretical guarantees.

Related Publications

Journal

Theoretical Guarantees for Domain Adaptation with Hierarchical Optimal Transport

El Hamri, Bennani, Falih

Machine Learning Journal, 2025

Machine Learning Journal
Conference

McCann’s Interpolation for Gradual Domain Adaptation on the Wasserstein Geodesic

El Hamri, Falih, Rozenholc

IJCNN 2025 — IEEE International Joint Conference on Neural Networks

IEEE
Conference

Hierarchical Representation for Multi-Source Domain Adaptation Using Wasserstein Barycenter

El Hamri, Falih, Rozenholc

ICMLA 2024 — International Conference on Machine Learning and Applications

IEEE
Journal

Incremental Confidence Sampling with Optimal Transport for Domain Adaptation

El Hamri, Bennani, Falih

International Journal of Neural Systems, 2024

World Scientific
Journal

Hierarchical Optimal Transport for Unsupervised Domain Adaptation

El Hamri, Bennani, Falih

Machine Learning Journal, 2022

Machine Learning Journal
Book Chapter

An Optimal Transport Framework for Collaborative Multi-View Clustering

Ben Bouazza, Bennani, El Hamri

Recent Advancements in Multi-View Data Analytics, 2022

Springer
Conference

Label Propagation Through Optimal Transport

El Hamri, Bennani, Falih

IJCNN 2021 — IEEE International Joint Conference on Neural Networks

IEEE