Frank Hutter

Founder of @Prior_Labs, Prof of #machinelearning at ELLIS Institute Tübingen & Uni Freiburg, 3x ERC Grant holder, EurAI & ELLIS fellow. All opinions are my own.

[Frank Hutter @FrankRHutter]

The data science revolution continues.
TabPFN-3.5 is live, claiming SOTA beyond the vanilla IID small data setting and tackling data complexities that data scientists face everyday:
✅  grouped data
✅  temporal data
✅  strings in tables
✅  strong uncertainty calibration
✅  one model, whether it's classification or regression, 100 or 1.000.000 rows
✅  scaling to 20k features (benchmarked thoroughly to 6k)
✅  up to 6x faster inference with TabPFN-3.5-fast, plus FP-8 support on API

Model report: https://t.co/Rn9pZg6Rb4

Try it today!
👉 Open-source repo: https://t.co/9kazbUqKdy
👉 API: https://t.co/DaTmuoGMUD

[Frank Hutter @FrankRHutter]

It was great to be on @MLStreetTalk, speaking about tabular foundation models with Tim Scarfe. 2 hours of great questions about TabPFN!

Full episode: https://t.co/XIWuy33EvG

[Machine Learning Street Talk @MLStreetTalk]

> "Tabular data is very dirty. I think that's one of the reasons deep learning took so long to do well for it."

We spoke with @FrankRHutter of @prior_labs about TabPFN, a foundation model that makes predictions on tables in one forward pass, trained entirely on synthetic data!

[Frank Hutter @FrankRHutter]

@jakestone2306 Good point, we built one now: https://t.co/F7Y6sN15e9

[Frank Hutter @FrankRHutter]

The data science revolution continues.
TabPFN-3.5 is live, claiming SOTA beyond the vanilla IID small data setting and tackling data complexities that data scientists face everyday:
✅  grouped data
✅  temporal data
✅  strings in tables
✅  strong uncertainty calibration
✅  one model, whether it's classification or regression, 100 or 1.000.000 rows
✅  scaling to 20k features (benchmarked thoroughly to 6k)
✅  up to 6x faster inference with TabPFN-3.5-fast, plus FP-8 support on API

Model report: https://t.co/Rn9pZg6Rb4

Try it today!
👉 Open-source repo: https://t.co/9kazbUqKdy
👉 API: https://t.co/DaTmuoGMUD

[Frank Hutter @FrankRHutter]

Super excited about our first work on relational learning, continuing to push on open source!

[Prior Labs @prior_labs]

We’re happy to announce our first release in relational learning at Prior Labs, continuing our commitment to open science.

We open-source three pieces of software that we expect to accelerate research in the field towards meaningful, real-world impact.

First and foremost, we release 𝗥𝗲𝗹𝗔𝗿𝗲𝗻𝗮-α: a unified framework for running and comparing baselines on RelBench v1 tasks. Based on learnings from tabular benchmarks like TabArena, we are standardizing data loading, evaluation protocols, tuning regimes, and adding support for systems with custom tuning.

We also open-source 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹: our relational harness for TabPFN-3. We initialize the (living) RelArena-α leaderboard with TabPFN-Rel and a comprehensive set of baselines. The rankings at the time of release are:

• 𝗧𝗮𝗯𝗣𝗙𝗡-𝗥𝗲𝗹 is the No. 1 model submission
• 𝗥𝗧-𝗣𝗹𝘂𝗥𝗲𝗹 is the No. 1 system submission

Last but not least, we open-source an alpha version of the 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 (𝗥𝗣𝗜): enabling you to easily specify prediction tasks on your own relational database and then run any RelArena-α model, like TabPFN-Rel, in a few lines of code, all bundled as a simple PyPI package.

• Read the full model report: https://t.co/hZqnpLacUN
• GitHub repository (give us a ⭐️): https://t.co/3C5dXDLlTo
• Announcement: https://t.co/ZegjuGbZ8b
• Docs: https://t.co/WVeEX89KGP

Thanks to the contributions from: @adrihayler, @KNfloege, @AlanArazi1536, @_rishabhranjan_, @jure, @LennartPurucker, @FrankRHutter & @noahholl

[Frank Hutter @FrankRHutter]

@NandoDF @bing @Azure @satyanadella Best of luck in what’s to come next @NandoDF!

[Frank Hutter @FrankRHutter]

@prior_labs @UniFreiburg @ELLISInst_Tue Here is the team that made this happen!

[Frank Hutter @FrankRHutter]

Today it is official: our acquisition by SAP has closed. Prior Labs continues as an independent frontier AI lab - same team, same offices, same mission, open models, backed by €1B+. What changes? The scale and the planning horizon. 🧵 1/4

[Frank Hutter @FrankRHutter]

4/4 Huge thanks to everyone at @prior_labs, @UniFreiburg, @ELLISInst_Tue and the community that got us here. Let’s go and take things to the next level!

Founders’ statement: https://t.co/PbX0Z16Don

[RAISE Summit @RaiseSummit]

Deep learning transformed how machines understand text and images.

But the data that actually runs most enterprises, the rows and columns inside spreadsheets and databases, has largely missed that revolution.

For decades, structured data has powered decisions across healthcare, finance, and business, yet it's been analyzed using many of the same approaches long after AI advanced everywhere else.

That is beginning to change.

A new generation of Tabular Foundation Models is bringing frontier AI to structured data, delivering faster, more accurate predictions without the task-specific training traditional models require. It's already powering real-world applications, from early disease detection to financial risk modelling, and redefining what's possible with enterprise data.

This July 8, @FrankRHutter, Founder & CEO at @Prior_Labs, takes the Grace Hopper Stage for his keynote, "The Foundation Model Revolution for Structured Data."

Drawing on the latest advances behind Prior Labs' TabPFN, he'll explore where the technology stands today, why this represents a fundamental shift for enterprise AI, and what's next for the future of structured data.

If foundation models transformed text and images, what happens when they finally transform the data your business actually runs on?

Final ticket price increase on July 2.

🔗 Secure your ticket:  https://t.co/20y5yW3HhL

[Frank Hutter @FrankRHutter]

Model report: https://t.co/oxOGqxcPkp
Get started here: https://t.co/62kIoImqpS

[Frank Hutter @FrankRHutter]

The data science revolution is here now.
TabPFN-3 is live, taking tabular foundation models to enterprise scale 🤩
1M training rows on a single H100. No training. No tuning. Load and predict.
🧵 1/5
#tabpfn #tabularfoundationmodels #priorlabs

[Frank Hutter @FrankRHutter]

Available three ways:
👉 Open weights for research & internal evaluation
👉 API with TabPFN-3-Plus & Thinking Mode
👉 Enterprise licensing (incl. AWS SageMaker, Azure AI Foundry, on-prem)

Model report in replies.
🧵 5/5
With ❤️ from Prior Labs

[Frank Hutter @FrankRHutter]

Huge news: @prior_labs has signed a definitive agreement to be acquired by @SAP.

€1B+ invested over four years to build a globally-leading frontier AI lab for structured data — in Europe, in the open.

Independent entity. Same team, same mission, same open models. A massive boost to what we can do. The mission just got accelerated.

Founders’ statement: https://t.co/7ZEi7q8a8l

(Deal subject to regulatory approval; terms not disclosed.)

[Frank Hutter @FrankRHutter]

This is a special moment for us at @prior_labs.
I’ve followed @ylecun’s work since the start of my career. His relentless focus on "what’s next" in AI has always been an inspiration. Today, I’m proud to say that he is joining us as a scientific advisor to help build the future of Tabular Foundation Models.

Yann understands that while LLMs have captured the world's attention, they have severe limitations. The vast majority of the world's data is tabular and LLMs are terrible with statistics and numbers. Our model, #TabPFN, fills this void, and the progress we are seeing is exponential.

A warm welcome to the team, Yann. It’s an honor to work with you alongside @bschoelkopf, Madelon Hulsebos, and @SamuelMullr on this powerhouse board. #DeepLearning #DataScience #TabularData #AI

[Prior Labs @prior_labs]

Honored to announce that Yann LeCun @ylecun is joining Prior Labs’ Scientific Advisory Board.

[Frank Hutter @FrankRHutter]

4/4 We’re currently working directly with customers to run TabPFN-2.5 Scaling Mode on their data.
If you’re interested, please contact [email protected] to get on the waitlist.

[Frank Hutter @FrankRHutter]

The tabular data science revolution is really here now! I’m thrilled to introduce *TabPFN Scaling Mode*, removing TabPFN’s previous limit on dataset size.
The plot below shows scaling results for the biggest dataset we have tried so far (subsamples of 100k and 1M, and the full 10M rows / data points). While we’ve only benchmarked up to 10M rows (a 100x increase in the 3 weeks since the release of TabPFN-2.5!), the results indicate that we can just scale to any dataset size now 🚀 🧵1/4

[Frank Hutter @FrankRHutter]

3/4 More details in our blog post: https://t.co/mzcbCk0R7a