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Show HN: TabPFN v2 – A SOTA foundation model for small tabular data | Hacker News

There are already early examples of this, that we discuss, in areas as diverse as biology, Bayesian optimization, time-series forecasting, and tabular data. The most prominent being TabPFN (Nature '25). 5/n news.ycombinator.com/item?id=4264...

news.ycombinator.com

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We go into detailed comparisons to other Bayesian methods and the trade-offs that lead us to the conclusion, that PFNs will become dominant for Bayesian prediction, and further that Bayesian prediction will become more important overall with better priors. 4/n

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What's nice is that the model after training on this random data, will start to make sense of real-world data, too. It will approximate the posterior belonging to the prior of choice, e.g., a BNN, a GP, or in the most interesting cases a Bayesian model that doesn't exist yet. 3/n

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Prior-data fitted networks (PFNs) do just that! The PFN idea is to use a prior, e.g. a bayesian neural network (BNN) prior, sample datasets from that prior, and then train to predict the hold-out labels of these datasets. (no training on real-world data) 2/n

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Samuel Müller @sammuller.bsky.social · 28/04/2025

Compute is increasing much faster than data. How can we improve classical supervised learning long term (the underlying tech of most of GenAI)? Our ICML position paper's answer: simply train on a bunch of artificial data (noise) and only do inference on real-world data! 1/n

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I am so proud to co-organize the workshop on foundation models for structured data at ICML. At this workshop, we will discuss on how to further extend the GenAI revolution to tabular data, time series forecasting etc. Consider submitting your work by May 19! icml-structured-fm-workshop.github.io

icml-structured-fm-workshop.github.io

Foundation Models for Structured Data

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Samuel Müller @sammuller.bsky.social · 25/04/2025

Could it be that @fchollet.bsky.social is not Francois Chollet?? They have a lot of ML followers 😅

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Samuel Müller @sammuller.bsky.social · 15/04/2025

To then change it? In like „overhaul“?

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GitHub - SamuelGabriel/LMARENA-GAMING

Find my full write up (including scenarios with bad actors, as well as the prompts used) plus the game here: github.com/SamuelGabrie... If you think, my single person experiment is not to be trusted? You are right, try it yourself!

github.com

Contribute to SamuelGabriel/LMARENA-GAMING development by creating an account on GitHub.

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In combination with the large employee numbers at top AI labs and small numbers of votes on lmarena lead me to the conclusion that lmarena scores are probably dominated by biased votes.

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In hard mode I attributed 13/20 completely correctly, much higher than the expected 3.3 of random guessing. That is I could identify all 3 models correctly in 13/20 cases after practicing with 20 questions. That means attributing responses to LLMs is super easy for humans.

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I first played easy mode (see below), where I got two answers from each model and need to match them. I used 20 interactions in the easy mode to learn the models' behaviors. In hard mode (see prev post), you need to match three responses to the LLM name.

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Second, employees are very likely able to tell models apart based on their gut feeling. To figure out if this is the case, I created a game with two modes. The game is about identifying which answer was provided by which LLM.

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First, AI labs have enough employees to bias the benchmarks. E.g. Grok 3 only has 10K votes and there are 2.7M votes in total on lmarena. If half of e.g. OpenAI (2,000 employees) voted just once a day, they would make up > 10% of all 2.7M lmarena votes over its one-year existence.

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Chatbot Arena (formerly LMSYS): Free AI Chat to Compare & Test Best AI Chatbots

I believe lmarena.ai scores are not to be trusted, as the people voting are likely to come from the AI labs in the leaderboard and push their own models unintentionally. A thread 🧵

lmarena.ai

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Samuel Müller @sammuller.bsky.social · 23/02/2025

How wrong do you think are the lmarena scores? Grok must be very easy to distinguish from other models in a blind evaluation

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Samuel Müller @sammuller.bsky.social · 07/02/2025

seems to beat boosting there, too, but prob a bit early to make definitive statements

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Samuel Müller @sammuller.bsky.social · 07/02/2025

TabPFN for Chemistry, check it out

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Samuel Müller @sammuller.bsky.social · 26/01/2025

What did you think was interesting? The interview had such bad timing, a few days before the r1 launch

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Samuel Müller @sammuller.bsky.social · 15/01/2025

MiniMax-01 takeaways - 7 of 8 layers are linear att - implemented a flash-variant of linear attention + ring-att - post-norm is back in large models! (using deepnorm) - prob. wrong scaling laws, as lr schedule is not adapted (see Chinchilla) Let's see how it fares in the arena!

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Samuel Müller @sammuller.bsky.social · 09/01/2025

Thank you :) So far, we only open source the model itself and how to use it. We do not open source how to train it exactly, sorry for that :| there is a company starting based on the model, thus it is kinda its mode

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Reposted by Samuel Müller

Víctor @victorbcn.bsky.social · 09/01/2025

Los modelos preentrenados para datos tabulares (TabPFN) podrían ser el nuevo state of the art para regresión y clasificación. 🙄 Habrá que probarlo. El GitHub al final del hilo. Éste en concreto es enorme y si se comporta como dicen es un gran salto adelante en el state of the art del campo.

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Reposted by Samuel Müller

leogrin.bsky.social @leogrin.bsky.social · 09/01/2025

Groundbreaking work, congrats to the team!! 🎉 When I started my PhD 3 years ago, our tabular benchmark showed tree-based models miles ahead of neural networks. On the same benchmark, TabPFN v2 now reaches in 10s what CatBoost achieves in 4h of tuning 🤯

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Reposted by Samuel Müller

Lennart Purucker @lennartpurucker.bsky.social · 09/01/2025

The tabular foundation model TabPFN v2 is finally public 🎉🥳 This is excellent news for (small) tabular ML! Checkout our Nature article (nature.com/articles/s41...) and code (github.com/PriorLabs/Ta...)

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GitHub - PriorLabs/TabPFN: ⚡ TabPFN: Foundation Model for Tabular Data ⚡

Thanks to all of my collaborators, without you this would not have been possible. 🙏🏼🙏🏼 If you want to use our model, check out the following: Nature article: nature.com/articles/s41... Try on free cloud: github.com/PriorLabs/ta... Try locally (gpu recommended): github.com/PriorLabs/Ta...

github.com

⚡ TabPFN: Foundation Model for Tabular Data ⚡. Contribute to PriorLabs/TabPFN development by creating an account on GitHub.

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The new TabPFN even outperforms Autogluon, which is a tool that mixes the best already existing methods (e.g. boosted trees and random forests). See plot (c) for classification results and (d) for regression results.

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How did we do this? We train a neural network that natively handles tables, using attention across rows and columns, on millions of artificial tabular datasets from a meticulously designed data generator. It then performs in-context learning to make predictions on unseen data.

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The new TabPFN is clearly the best single method for all regression and classification problems up to 10,000 data points and 500 features in our eval, no other limits. Not only that, it requires only a few seconds to train on new data, while the baselines are tuned for 4 hours.

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