TabPrep is a new feature engineering pipeline that targets three data patterns and improves performance of tree-based, neural, linear, and foundation models on tabular benchmarks, often more than model architecture changes.
Tabpfn unleashed: A scalable and effective solution to tabular classification problems
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TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.
VIP-COP is a black-box method that optimizes context for tabular foundation models by ranking and selecting high-value samples and features via online KernelSHAP regression, outperforming baselines on large high-dimensional data.
citing papers explorer
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TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
TabPrep is a new feature engineering pipeline that targets three data patterns and improves performance of tree-based, neural, linear, and foundation models on tabular benchmarks, often more than model architecture changes.
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TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
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On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Tabular foundation models suffer from test-time adversarial vulnerabilities that degrade accuracy and enable transferable attacks, but incremental adversarial in-context learning improves robustness on multiple benchmarks.
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VIP-COP: Context Optimization for Tabular Foundation Models
VIP-COP is a black-box method that optimizes context for tabular foundation models by ranking and selecting high-value samples and features via online KernelSHAP regression, outperforming baselines on large high-dimensional data.