🧑‍💻🧑‍🏫 I'm recruiting a post-doc to work on Tabular Foundation Models, one of the hotest topics in AI, where we are at the leading edge team.inria.fr/soda/files/2... This is an opportunity to develop the next-level tabular AI, blending deep learning and tables.

Here is the recording of the webinar I gave last week on GPU support in @scikit-learn.org and comparison of a scikit-learn pipeline vs the TabICLv2 foundational model on a non-linear heteroscedastic quantile regression task. app.livestorm.co/probabl/webi...

Olivier Grisel@ogrisel.bsky.social · 4mo ago

Tomorrow I will give an online demo of the use of the Python array API to develop a non-linear regression pipeline with GPU acceleration and uncertainty quantification. We will also introduce TabICLv2 and demo it on the same predictive tasks. Register here: www.linkedin.com/events/webin...

To piggy-back a bit on foundation models for structured data discussion here My colleagues at Yandex Research just updated the GraphPFN paper. It's a Graph Foundation Model that works on graph datasets with tabular features, and shows SOTA results both in ICL regimes and when fine-tuned.

GraphPFN: A Prior-Data Fitted Graph Foundation Model

Graph foundation models face several fundamental challenges including transferability across datasets and data scarcity, which calls into question the very feasibility of graph foundation models. Howe...

arxiv.org

Sung Kim@sungkim.bsky.social · 6mo ago

You’d think a large model for a tabular data is revolutionary, except we’ve been doing it for DECADES. techcrunch.com/2026/02/05/f...

Super hyped that it's finally out!

Gaël Varoquaux@gaelvaroquaux.bsky.social · 6mo ago

🎉 Announcing TabICLv2: State-of-the art Table Foundation Model, fast and open source A breakthrough for tabular ML: better prediction and faster runtime than alternatives, work by Jingang Qu, David Holzmüller @dholzmueller.bsky.social , Marine Le Morvan, and myself 👇

My 2025 highlights for AI research and code: ▪ Unpacking the AI scale narrative ▪ Tabular-learning research - TabICL: table foundation model - Retrieve merge predict: data lakes ▪ Better software - Skrub: machine learning with tables - Fundamentals in scikit-learn gael-varoquaux.info/science/2025...

2025 highlights: AI research and code

AI is everywhere. Can you see it here? Note Some highlights about my work in 2025: progress on tabular-learning stands out, a publication on unpacking trade-off and consequences of scale in...

gael-varoquaux.info

Sacha Braun, David Holzm\"uller, Michael I. Jordan, Francis Bach Conditional Coverage Diagnostics for Conformal Prediction https://arxiv.org/abs/2512.11779

Let's kick off 2026 with a workshop on Survival Analysis and Foundation Models, co-organized w. Julie Alberge Linus Bleistein Clément Berenfeld Agathe Guilloux and Julie Josse on January 27th at PariSanté Campus ! Registrations and submissions are open!!! www.linusbleistein.com/ramh

Still using temperature scaling? With @dholzmueller.bsky.social, Michael I. Jordan and @bachfrancis.bsky.social we argue that with well designed regularization, more expressive models like matrix scaling can outperform simpler ones across calibration set sizes, data dimensions, and applications.

⚡ Release 0.6.2 is out ⚡ github.com/skrub-data/s...

Release 0.6.2 · skrub-data/skrub

New features The DataOp.skb.full_report() now displays the time each node took to evaluate. #1596 by Jérôme Dockès. The User guide has been reworked and expanded. Changes and deprecations Ken em...

github.com

Daniel Beaglehole, David Holzm\"uller, Adityanarayanan Radhakrishnan, Mikhail Belkin: xRFM: Accurate, scalable, and interpretable feature learning models for tabular data https://arxiv.org/abs/2508.10053 https://arxiv.org/pdf/2508.10053 https://arxiv.org/html/2508.10053

I got 3rd out of 691 in a tabular kaggle competition – with only neural networks! 🥉 My solution is short (48 LOC) and relatively general-purpose – I used skrub to preprocess string and date columns, and pytabkit to create an ensemble of RealMLP and TabM models. Link below👇

Excited to have co-contributed the SquashingScaler, which implements the robust numerical preprocessing from RealMLP!

Skrub@skrub-data.bsky.social · last yr.

⚡ Release 0.6.0 is now out! ⚡ 🚀 Major update! Skrub DataOps, various improvements for the TableReport, new tools for applying transformers to the columns, and a new robust transformer for numerical features are only some of the features included in this release.

👨‍🎓🧾✨#icml2025 Paper: TabICL, A Tabular Foundation Model for In-Context Learning on Large Data With Jingang Qu, @dholzmueller.bsky.social, and Marine Le Morvan TL;DR: a well-designed architecture and pretraining gives best tabular learner, and more scalable On top, it's 100% open source 1/9

🚨What is SOTA on tabular data, really? We are excited to announce 𝗧𝗮𝗯𝗔𝗿𝗲𝗻𝗮, a living benchmark for machine learning on IID tabular data with: 📊 an online leaderboard (submit!) 📑 carefully curated datasets 📈 strong tree-based, deep learning, and foundation models 🧵

Missed the school? We have uploaded recordings of most talks to our YouTube Channel www.youtube.com/@AutoML_org 🙌

AutoML Freiburg Hannover Tübingen

This channel features videos about automated machine learning (AutoML) from the AutoML groups at the University of Freiburg, Leibniz University Hannover and University of Tübingen. Common topics inclu...

youtube.com

📝 The skrub TextEncoder brings the power of HuggingFace language models to embed text features in tabular machine learning, for all those use cases that involve text-based columns.

🚨ICLR poster in 1.5 hours, presented by @danielmusekamp.bsky.social : Can active learning help to generate better datasets for neural PDE solvers? We introduce a new benchmark to find out! Featuring 6 PDEs, 6 AL methods, 3 architectures and many ablations - transferability, speed, etc.!

The Skrub TableReport is a lightweight tool that allows to get a rich overview of a table quickly and easily. ✅ Filter columns 🔎 Look at each column's distribution 📊 Get a high level view of the distributions through stats and plots, including correlated columns 🌐 Export the report as html

#ICLR2025 Marine Le Morvan presents "Imputation for prediction: beware of diminishing returns": poster Thu 24th arxiv.org/abs/2407.19804 Concludes 6 years of research on prediction with missing values: Imputation is useful but improvements are expensive, while better learners yield easier gains.

Excited to share the new monthly Table Representation Learning (TRL) Seminar under the ELLIS Amsterdam TRL research theme! To recur every 2nd Friday. Who: Marine Le Morvan, Inria (in-person) When: Friday 11 April 4-5pm (+drinks) Where: L3.36 Lab42 Science Park / Zoom trl-lab.github.io/trl-seminar/

We are excited to announce #FMSD: "1st Workshop on Foundation Models for Structured Data" has been accepted to #ICML 2025! Call for Papers: icml-structured-fm-workshop.github.io

Trying something new: A 🧵 on a topic I find many students struggle with: "why do their 📊 look more professional than my 📊?" It's *lots* of tiny decisions that aren't the defaults in many libraries, so let's break down 1 simple graph by @jburnmurdoch.bsky.social 🔗 www.ft.com/content/73a1...

🚀Continuing the spotlight series with the next @iclr-conf.bsky.social MLMP 2025 Oral presentation! 📝LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators 📷 Join us on April 27 at #ICLR2025! #AI #ML #ICLR #AI4Science

Practitioners are often sceptical of academic tabular benchmarks, so I am elated to see that our RealMLP model outperformed boosted trees in two 2nd place Kaggle solutions, for a $10,000 forecasting challenge and a research competition on survival analysis.

A new tabular classification benchmark provides another independent evaluation of our RealMLP. RealMLP is the best classical DL model, although some other recent baselines are missing. TabPFN is better on small datasets and boosted trees on larger datasets, though.

Learning rate schedules seem mysterious? Why is the loss going down so fast during cooldown? Turns out that this behaviour can be described with a bound from *convex, nonsmooth* optimization. A short thread on our latest paper 🚞 arxiv.org/abs/2501.18965

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training

We show that learning-rate schedules for large model training behave surprisingly similar to a performance bound from non-smooth convex optimization theory. We provide a bound for the constant schedul...

arxiv.org

Early stopping on validation loss? This leads to suboptimal calibration and refinement errors—but you can do better! With @dholzmueller.bsky.social, Michael I. Jordan, and @bachfrancis.bsky.social, we propose a method that integrates with any model and boosts classification performance across tasks.

The first independent evaluation of our RealMLP is here! On a recent 300-dataset benchmark with many baselines, RealMLP takes a shared first place overall. 🔥 Importantly, RealMLP is also relatively CPU-friendly, unlike other SOTA DL models (including TabPFNv2 and TabM). 🧵 1/

Join us on 27 Feb in Amsterdam for the ELLIS workshop on Representation Learning and Generative Models for Structured Data ✨ sites.google.com/view/rl-and-... Inspiring talks by @eisenjulian.bsky.social, @neuralnoise.com, Frank Hutter, Vaishali Pal, TBC. We welcome extended abstracts until 31 Jan!

My book is (at last) out, just in time for Christmas! A blog post to celebrate and present it: francisbach.com/my-book-is-o...