SKADA-Bench : Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities, has been published published in TMLR today 🚀. It was a huge team effort to design (and publish) an open source fully reproducible DA benchmark 🧵1/n. openreview.net/forum?id=k9F...

Our EEG montage interpolation method is now in MNE-Python 1.10! Based on our EUSIPCO 2024 paper, .interpolate_to() maps signals across caps in one line—ideal for preprocessing EEG before training AI models across datasets. 📄 arxiv.org/abs/2403.15415 🧠 mne.tools/stable/auto_... #EEG #MNEPython #AI

Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets

Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at...

arxiv.org

Skada Sprint Alert: Contribute to Domain Adaptation in Python 📖 Machine learning models often fail when the data distribution changes between training and testing. That’s where Domain Adaptation comes in — helping models stay reliable across domains.

Opinion of the day: we don't desk reject enough in ML. Too much energy is wasted in 4x reviewing papers that will *obviously* be rejected. Second opinion otd: we don't teach enough students to be positive. We should not seek how to reject a paper, but how to accept it. And yes, #1 has a role in #2

We have been reworking the Quickstart guide of POT to show multiple examples of OT with the unified API that facilitates access to OT value/plan/potentials. It allows to select regularization/unbalancedness/lowrank/Gaussian OT with just a few parameters. pythonot.github.io/master/auto_...

It's been 20 years and I think the new generation need to know about the SVM-KM toolbox. It was a Matlab open source SVM toolbox created in 2005 by @scanu.bsky.social, Yves Grandvalet, Vincent Guige, and Alain Rakotomamonjy 1/n github.com/rflamary/SVM...

We put lots of effort to benchmark domain adaptation on many modalities👇🏻👇🏻

Théo Gnassounou@tgnassou.bsky.social · last yr.

🚀 I’m pleased to announce a new preprint! "SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities" 📢 Check it out & contribute! 📜 Paper: arxiv.org/abs/2407.11676 💻 Code: github.com/scikit-adapt...

Merci @lemonde.fr pour un joli résumé de mes aventures scientifiques et logiciels 📈📠 www.lemonde.fr/sciences/art... Beaucoup de messages qui me tiennent à cœur : travail d'équipe, logiciel libre, rigueur scientifique Merci aux collègues et amis qui ont témoigné, je suis ému de lire

Gaël Varoquaux, vedette de l’intelligence artificielle et défenseur du logiciel libre

L’informaticien et chercheur à l’Inria est l’expert français le plus cité dans les publications scientifiques portant sur l’IA. Avec Scikit-learn, un programme de machine learning dont il est le cocré...

lemonde.fr

Super proud of this work! DA is the way to go for many applications and Skada will democratize it!

Théo Gnassounou@tgnassou.bsky.social · 2y ago

🚀 Skada v0.4.0 is out! Skada is an open-source Python library built for domain adaptation (DA), helping machine learning models to adapt to distribution shifts. Github: github.com/scikit-adapt... Doc: scikit-adaptation.github.io DOI: doi.org/10.5281/zeno... Installation: `pip install skada`

🚀 Skada v0.4.0 is out! Skada is an open-source Python library built for domain adaptation (DA), helping machine learning models to adapt to distribution shifts. Github: github.com/scikit-adapt... Doc: scikit-adaptation.github.io DOI: doi.org/10.5281/zeno... Installation: `pip install skada`

Next week, we’ll present our spotlight paper at #NeurIPS2024 on domain adaptation for EEG data. Join us in East Exhibit Hall A-C on Friday at 4:30 PM! arxiv.org/abs/2407.03878 Apolline Mellot @sylvchev.bsky.social @agramfort.bsky.social @dngman.bsky.social A thread: 1/7

Geodesic Optimization for Predictive Shift Adaptation on EEG data

Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the data $X$ and in the b...

arxiv.org

Good, published, benchmarks of machine learning / data science is crucial. But so hard. Well-cited "SOTA" methods typically crash often. They tend to be very computational expensive. Both make a systematic study impossible. Finally, reviewers always ask for more methods, and more "SOTA".

Apolline Mellot, Antoine Collas, Sylvain Chevallier, Alexandre Gramfort, Denis A. Engemann Geodesic Optimization for Predictive Shift Adaptation on EEG data https://arxiv.org/abs/2407.03878