表格基础模型的保形预测与条件覆盖
Conformal Prediction and Conditional Coverage for Tabular Foundation Models
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中文总结 AI 辅助
针对表格基础模型预测区域覆盖不准确的问题，提出C-USIM方法，无需额外训练即可实现有限样本边际有效并降低条件覆盖误差，实验验证其有效性。
中文摘要 AI 辅助
表格基础模型（TFMs）为回归任务提供预测分布，但其预测区域即使在点预测准确的情况下也可能出现欠覆盖或过覆盖现象。我们提出了C-USIM（条件均匀化分数集成方法），这是一种轻量级应用最高预测密度分裂保形预测的方法，能够适应多模态预测。给定校准和测试输出，它无需额外训练或模型推理。在我们的假设下，它提供有限样本边际有效性。我们利用分布估计误差和分数离散性来界定条件边际覆盖差距，并通过百分位秩分数图检查覆盖异质性。使用TabPFN和TabICL进行的实验表明，边际覆盖准确性得到改善，平均条件和组覆盖误差降低。在固定数据预算下，将更多观测分配给校准可以减少边际覆盖误差，尽管点预测准确性较低。
英文摘要
Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcoverage even when point predictions are accurate. We introduce C-USIM (Conditionally-Uniformized Score Integration Method), a lightweight application of highest predictive density split conformal prediction that accommodates multimodal predictions. Given calibration and test outputs, it requires no additional training or model inference. It provides finite-sample marginal validity under our assumptions. We bound conditional-marginal coverage gaps using distribution-estimation error and score discreteness, and examine coverage heterogeneity through percentile rank-score plots. Experiments with TabPFN and TabICL show improved marginal coverage accuracy and lower average conditional and group coverage errors. Under a fixed data budget, allocating more observations to calibration can reduce marginal coverage error despite less accurate point predictions.
发表机构
- Seoul National University（首尔大学）
机构由 AI 辅助整理，请以论文原文为准。
