As the SIERRA team of @inria_paris, we will be presenting 11 papers at @NeurIPSConf 2021 and a Test of Time Award! + Very happy to mention that one of our papers received an Outstanding Paper Award! See the thread below for details + join us at the posters if interested! 1/13

What are good optimizers for diffusion models? 🍂 TLDR: Muon and SOAP are very good. Paper: https://t.co/TYqRpfcu5t

Bravo Etienne!

I am honored to be a 2025 Google PhD Fellow in Machine Learning & ML Foundations 🥳 Grateful to @Googleorg for their incredible support, my advisors @BachFrancis & Michael I. Jordan, and my lab at @Inria & @ENS_ULM for fostering a space to think deeply.

Together with the great Frederik Kunstner ( https://t.co/PP2JY3QqK4)

Not all scaling laws are nice power laws. This month’s blog post: Zipf’s law in next-token prediction and why Adam (ok, sign descent) scales better to large vocab sizes than gradient descent: https://t.co/uoy5GPrZek

Tired of lengthy computations to derive scaling laws? This post is made for you: discover the sharpness of the z-transform! https://t.co/k1CJu0CVoR

Happy to have our recent papers on conformal prediction with e-values presented at COLT by my advisor @BachFrancis! Full details here: 📚 https://t.co/exKCl7g5Pf 📚 https://t.co/YFtmxeVTtN #COLT2025

For good probability predictions, you should use post-hoc calibration. With @Eugene_Berta, Michael Jordan, and @BachFrancis we argue that early stopping and tuning should account for this! Using the loss after post-hoc calibration often avoids premature stopping. 🧵1/

I’ll be presenting our paper at COLT in Lyon this Monday at the Predictions and Uncertainty workshop — come say hi if you're around! 👋 Check out @DHolzmueller's thread below 👇 #COLT2025

Characterizing finely the decay of eigenvalues of kernel matrices: many people need it, but explicit references are hard to find. This blog post reviews amazing asymptotic results from Harold Widom (1963!) and proposes new non-asymptotic bounds. https://t.co/HrrAacOvCA

A free book: Learning Theory from First Principles by @BachFrancis It covers a bunch of key topics from machine learning (ML) theory and practice, such as: - Math basics - Supervised learning - Generalization, overfitting & adaptivity - Tools to design learning algorithms -

Michael Jordan is indeed one of the greatest thinkers in the history of AI 🐐 Economics, incentives (mechanism design), information flow, creativity, cooperation, greed and power struggles are important topics that we crucially need to understand better for the benefit of

[🔴#IAScienceSociety] @BachFrancis (@inria_paris-@ENS_ParisSaclay) participe au symposium "The Mathematics of #MachineLearning" aux côtés de @gabrielpeyre, Gilles Louppe (@UniversiteLiege), Gersende Fort (@CNRS) et Vianney Perchet (@INSAE @IP_Paris_).

🔴 @Inria est présent à la conférence "AI,Science and society" à @IP_Paris_, prélude au #SommetActionIA et sous la direction du Pr. Michael Jordan (@UCBerkeley-@Inria) ! L'événement a été inauguré ce matin par Michael Jordan et cet après-midi par @ClaraChappaz 🗣

My book is (at last) out, just in time for Christmas! A blog post to celebrate and present it: https://t.co/ttRo04efDF

Bravo Bertille et Marc!

🎓#PhDs | Félicitations à Juliette Murris et Alice Rogier de l’équipe @TeamHeka (@univ_paris_cite, @CRCordeliers, @Inserm) ainsi qu'à @oumaymabounou et Céline Moucer des équipes WILLOW et @Sierra_ML_Lab (@CNRSinformatics, @ENS_ULM) qui ont soutenu leur #thèse en octobre 🎉👏 !

#ERCStG 🏆 | Félicitations à @TaylorAdrien, chargé de recherche @inria au centre @inria_paris, membre de l'équipe-projet commune @Sierra_ML_Lab (@CNRS @CNRSinformatics @ENS_ULM), lauréat d'une ERC Starting Grant 👏 Découvrez son portrait et son projet 👉 https://t.co/l0gGdsrcSd

How fast is gradient descent, *for real*? Some (partial) answers in this new blog post on scaling laws for optimization. https://t.co/qFpIX2EIZv

Bravo Adrien!