PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.
TabPFN: A transformer that solves small tabular classification problems in a second
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Distilling TabICLv2 into XGBoost via stratified OOF labeling yields 0.882 macro-mean AUC (96.5% of teacher) at 1.9 ms CPU across 153 datasets, with significant gains over tuned CatBoost on low-dimensional data.
FICBO pretrains a feedback-aware transformer with a structured prior on feedback distortion to adaptively exploit or ignore unreliable auxiliary signals during in-context black-box optimization.
TabCF is a tuning-light method using tabular foundation models for control function regression to estimate distributional causal effects such as interventional means and quantiles.
Context construction strategies such as balanced sampling improve AUC-ROC by 3-4 points over uniform sampling in tabular foundation models for credit risk, exceeding differences between model families and matching classical baselines.
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PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks
PFN-TS converts PFN posterior predictives into mean-reward samples for Thompson sampling using a subsampled predictive CLT, with consistency proofs, regret bounds, and strong empirical performance on synthetic and real bandit benchmarks.