TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second · HackerPapers discussions

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second — mentioned by _giorgio_

Thu, 09 Jan 2025 23:03:54 GMT

It's probably the same model with the same limitations, released nearly two years ago? https://arxiv.org/abs/2207.01848

Paper and discussion history. A mention is not an endorsement.

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second — mentioned by westurner

Sat, 26 Aug 2023 17:09:25 GMT

There are multiple metrics to optimize for when optimizing. FWIU, from the diagram in the photo in the linked tweet, which is similar to a diagram on page 16 of the TabPFN paper [1], on the OpenML-CC18, TabPFN has a better ROC Receiver Operating Characteristic after 1 second than XGboost, Catboost, LightGBM, KNN, SAINT, Reg. Cocktail, and Autogluon after any amount of time, but Auto-sklearn 2.0 required 5 minutes to reach ~ROC parity with TabPFN. 1. "TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second" (May 2023) https://arxiv.org/abs/2207.01848 2.…

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TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second — HN submission

Sat, 22 Oct 2022 03:25:02 GMT

A Transformer That Solves Small Tabular Classification Problems in a Second

2 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second — mentioned by ersiees

Wed, 03 Aug 2022 19:12:56 GMT

Our lab works on changing this. I think it might still take some years for a full solution, but so far we are successful with NNs for small datasets with meta-learning (https://arxiv.org/abs/2207.01848) and large datasets with regularisation (https://arxiv.org/abs/2106.11189). The first is very new, but the second is also cited in this paper, but they didn’t run it as a baseline.

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