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Related: CARTE-AI, which can also deal with multiple tables.

https://soda-inria.github.io/carte/ https://arxiv.org/pdf/2402.16785

The paper includes a comparison to TabPFN v1 (among others), noting the lack of categorical & missing values handling which v2 now seems to have. Would be curious to see an updated comparison.

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TabPFN is better on numerical data since v1 (see figure 6 in the CARTE paper). CARTE's main strength in on text features, which are now also supported for TabPFN v2 API version (https://github.com/PriorLabs/tabpfn-client). We compared this to CARTE and found our model to be generally quite better, and much faster. CARTE multi-table approach is also very interesting, and we want to tackle this setting in the future.

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