AI RESEARCH
End-to-End Compression for Tabular Foundation Models
arXiv CS.LG
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ArXi:2602.05649v2 Announce Type: replace The long-standing dominance of gradient-boosted decision trees for tabular data has recently been challenged by in-context learning tabular foundation models. In-context learning methods fit and predict in one forward pass without parameter updates by leveraging the