🚀 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...

🚀 Meet skore—the @scikit-learn.org sidekick!

🔹 Offers guidance on modeling
🔹 Automated reports & key metrics
💡 Built by scikit-learn maintainers. Open-source & ready to use!

Try it 👉 tinyurl.com/bdhszwtn

With some help (and terrifying but so nice code reviews) from @agramfort.bsky.social we made it a proper Python library with a documentation and an example gallery. pythonot.github.io/auto_example...

Great science and friends moments this year at @neuripsconf.bsky.social see you next year 👋

I will be at @neuripsconf.bsky.social 2024 in Vancouver next week. Ping me if you want to grab ☕️ or🍺 and chat about ML stuff for biosignals (EMG, EEG, MEG, …)

See you there 🙌

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...

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

Anne Gagneux, Ségolène Martin, @quentinbertrand.bsky.social Remi Emonet and I wrote a tutorial blog post on flow matching: dl.heeere.com/conditional-... with lots of illustrations and intuition!

We got this idea after their cool work on improving Plug and Play with FM: arxiv.org/abs/2410.02423