Eduardo Fernandes Montesuma

Paris, France

I am an AI researcher specializing in optimal transport, domain adaptation, and transfer learning. I completed my PhD in Data Science at Université Paris-Saclay in 2024, where I focused on multi-source domain adaptation through Wasserstein barycenters. Prior to that, I earned a Master’s degree in Electronic Engineering from INSA de Rennes and a Bachelor’s in Computer Engineering from Universidade Federal do Ceará in Brazil. You can take a look on my recent publications at Google Scholar.

Currently, I work as an AI researcher at Sigma Nova since August 2025, focusing on fine-tuning and adapting large foundation models. My research bridges theoretical advances in optimal transport with practical machine learning applications, particularly in scenarios where models need to adapt across different domains or data distributions.

news

Mar 27, 2026	📄 Our paper ReBaPL: Repulsive Bayesian Prompt Learning was accepted at CVPR 2026.
Aug 01, 2025	🚀 Started a new role as AI Researcher at Sigma Nova, working on the fine-tuning and adaptation of large pre-trained foundation models.
May 01, 2025	📄 Optimal Transport for Domain Adaptation through Gaussian Mixture Models is now published in Transactions on Machine Learning Research (TMLR).
Apr 01, 2025	📄 Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport accepted at Transactions on Machine Learning Research (TMLR).
Mar 15, 2025	📄 Two papers accepted at Geometric Science of Information (GSI) 2025: KD²M: A Unifying Framework for Feature Knowledge Distillation and A Dimensionality Reduction Technique Based on the Gromov-Wasserstein Distance.

latest posts

May 04, 2022	Optimal Transport for Domain Adaptation

selected publications

CVPR
ReBaPL: Repulsive Bayesian Prompt Learning

Yassir Bendou, Omar Ezzahir, Eduardo Fernandes Montesuma, and 3 more authors

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026

Abs arXiv Bib PDF

Conventional prompt learning methods are prone to overfitting and can struggle with out-of-distribution generalization. We frame prompt optimization as a Bayesian inference problem and propose ReBaPL, which integrates a cyclical step-size schedule with a stochastic gradient Hamiltonian Monte Carlo (SGHMC) algorithm and a repulsive force derived from probability metrics (Maximum Mean Discrepancy, Wasserstein) to diversify exploration of the prompt posterior and prevent premature collapse to a single mode.
@inproceedings{bendou2026rebapl, title = {ReBaPL: Repulsive Bayesian Prompt Learning}, author = {Bendou, Yassir and Ezzahir, Omar and Fernandes Montesuma, Eduardo and Mahuas, Gabriel and Shevchenko, Victoria and Gartrell, Mike}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2026}, }

TMLR

Optimal Transport for Domain Adaptation through Gaussian Mixture Models

Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, and Antoine Souloumiac

Transactions on Machine Learning Research, 2025

arXiv Bib PDF

@article{montesuma2025optimal,
  title = {Optimal Transport for Domain Adaptation through Gaussian Mixture Models},
  author = {Fernandes Montesuma, Eduardo and Mboula, Fred Maurice Ngol{\`e} and Souloumiac, Antoine},
  journal = {Transactions on Machine Learning Research},
  issn = {2835-8856},
  year = {2025},
  url = {https://openreview.net/forum?id=DCAeXwLenB},
}

TPAMI
Recent Advances in Optimal Transport for Machine Learning

Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, and Antoine Souloumiac

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Abs arXiv Bib PDF

Optimal Transport (OT) is a probabilistic framework for comparing and manipulating probability distributions. We survey the contributions of OT to machine learning over 2012-2023 across supervised, unsupervised, transfer, and reinforcement learning, including computational OT, partial, unbalanced, Gromov, and Neural OT and their interplay with ML practice.
@article{montesuma2024recent, title = {Recent Advances in Optimal Transport for Machine Learning}, author = {Fernandes Montesuma, Eduardo and Mboula, Fred Maurice Ngol{\`e} and Souloumiac, Antoine}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, year = {2024}, publisher = {IEEE}, }

ECML-PKDD

Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport

Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, and Antoine Souloumiac

In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 2024

arXiv Bib PDF Code

@inproceedings{montesuma2024lighter,
  title = {Lighter, Better, Faster Multi-Source Domain Adaptation with Gaussian Mixture Models and Optimal Transport},
  author = {Fernandes Montesuma, Eduardo and Mboula, Fred Maurice Ngol{\`e} and Souloumiac, Antoine},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD)},
  pages = {21--38},
  year = {2024},
  organization = {Springer},
}

ECAI

Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space

Eduardo Fernandes Montesuma, Fred Maurice Ngolè Mboula, and Antoine Souloumiac

In 26th European Conference on Artificial Intelligence (ECAI), 2023

arXiv Bib PDF Code

@inproceedings{montesuma2023multi,
  title = {Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space},
  author = {Fernandes Montesuma, Eduardo and Mboula, Fred Maurice Ngol{\`e} and Souloumiac, Antoine},
  booktitle = {26th European Conference on Artificial Intelligence (ECAI)},
  pages = {1739--1746},
  year = {2023},
  publisher = {IOS Press},
}

CVPR
Wasserstein Barycenter for Multi-Source Domain Adaptation

Eduardo Fernandes Montesuma and Fred Maurice Ngolè Mboula

In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

Abs Bib PDF

Multi-source domain adaptation is a key technique that allows a model to be trained on data coming from various probability distributions. We propose a method for constructing an intermediate domain between sources and target domain via the barycenter on Wasserstein spaces (WBT). After aggregating sources, they are transported to the target domain using standard OT for Domain Adaptation. We evaluate on object recognition, face recognition, music genre recognition, and music-speech discrimination.
@inproceedings{montesuma2021wasserstein, title = {Wasserstein Barycenter for Multi-Source Domain Adaptation}, author = {Fernandes Montesuma, Eduardo and Mboula, Fred Maurice Ngol{\`e}}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, pages = {16785--16793}, year = {2021}, }