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.
LightGBM: A highly efficient gradient boosting decision tree
4 Pith papers cite this work. Polarity classification is still indexing.
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Leakage-aware distillation transfers at least 90% of tabular foundation model AUC to lightweight students across 19 health datasets, with 26x CPU speedup and preserved calibration/fairness.
TabH2O presents a unified tabular foundation model with dual-head architecture and single-stage pretraining that achieves an average rank of 2.55 on the TALENT benchmark, outperforming several established methods.
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.
citing papers explorer
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Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees
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.
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Distilling Tabular Foundation Models for Structured Health Data
Leakage-aware distillation transfers at least 90% of tabular foundation model AUC to lightweight students across 19 health datasets, with 26x CPU speedup and preserved calibration/fairness.
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TabH2O: A Unified Foundation Model for Tabular Prediction
TabH2O presents a unified tabular foundation model with dual-head architecture and single-stage pretraining that achieves an average rank of 2.55 on the TALENT benchmark, outperforming several established methods.
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Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models
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.