Frank Hutter

Machine Learning Street Talk Verified account @MLStreetTalk · Sep 23

> "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!

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Prior Labs Verified account @prior_labs · Aug 18

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: arxiv.org/abs/2608.16319
• GitHub repository (give us a ⭐️): github.com/PriorLabs/rela…
• Announcement: priorlabs.ai/blog-posts/int…
• Docs: docs.priorlabs.ai/capabilities/r…

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

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