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arXiv · 2609.23574

PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models

Qi Qin·Erbo Li·Ting Wei·Zizhou Huang·Zixuan Qin·Wu Wang·Yifan Sun
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Abstract

In high-dimensional tabular learning, feature screening provides a lightweight, model-agnostic way to remove irrelevant features before model fitting. However, scoring raw values directly can miss nonlinear or distributional structure. We introduce PACE (Plug-and-Play Contextual Embedding), which inserts a frozen tabular foundation model (TFM) column encoder before an existing feature-scoring rule, expanding each feature into a higher-dimensional contextual representation. Across controlled studies, PACE improves raw-space screening of complex nonlinear dependence with only modest additional encoding cost. These gains translate to downstream prediction on TALENT datasets: PACE-DC improves binary AUC by 0.077 and multiclass macro-AUC by 0.064, with a median normalized RMSE improvement of 0.063 across ten learners. Matched random-weight and random-feature controls show that PACE gains from pretrained structure beyond generic dimensional expansion. PACE further achieves favorable performance--time trade-offs against task-fitted selectors and attribution-based methods, positioning pretrained column geometry as a reusable upstream primitive for tabular learning.

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Qi Qin, Erbo Li, Ting Wei, Zizhou Huang, Zixuan Qin, Wu Wang, Yifan Sun. 2026-09-20. PACE: Plug-and-Play Contextual Embedding for Feature Screening with Pretrained Tabular Foundation Models. https://arxiv.org/abs/2609.23574

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