[{"_1":2,"_68":-5,"_69":-5},"loaderData",{"_3":4,"_38":39,"_66":-5,"_67":-5},"root",{"_5":-5,"_6":-5,"_7":8,"_22":23,"_24":25,"_26":27,"_30":31,"_36":37},"user","shoppingCartId","shoppingCart",{"_9":10,"_11":12,"_13":14,"_15":16,"_17":18,"_19":20,"_21":14},"id","no-cart","count",0,"free",true,"updatedAt","","paymentGateway","STRIPE","cartItems",[],"loading","locale","en","readerTheme","light","constants",{"_28":29},"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","publishers",{"_32":33,"_34":35},"companies",[],"universities",[],"needsLocaleCookieSync",false,"routes/_store+/_readers+/email_author.$slug/route",{"_40":41,"_65":-5},"book",{"_9":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzcxMTc","title","Foundation Models for Tabular Data","state","published","contactAuthorMethod","email","aboutText","

The first comprehensive reference on pretrained in-context learning models for structured data — from PFN theory to production deployment.

Gradient-boosted trees have dominated tabular ML for a decade. CatBoost, XGBoost, LightGBM. Every serious benchmark confirmed it.

That era is ending.

A new class of models — tabular foundation models — can make accurate predictions on previously unseen datasets without any dataset-specific training. No gradient descent on your data. No hyperparameter search. The training rows are the prompt.

This book is the definitive reference on the field. It covers the PFN framework and amortized Bayesian inference, seven architectural families (TabPFN-2.5, TabICLv2, Mitra, TabDPT, TabFlex, ConTextTab, TabSTAR), honest evaluation methodology that exposes benchmarking failures, deployment engineering including attention costs and KV caching, calibration, conformal prediction, and interpretability, and a practitioner decision framework for model selection.

Every claim is sourced. Every comparison is fair. Every architectural choice is explained from first principles.

If you're serious about production tabular ML, this is the book.

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