表格基础模型能否摊销统计推断？
Can Tabular Foundation Models Amortize Statistical Inference?
- London School of Economics and Political Science（伦敦政治经济学院）
- University of Science and Technology of China（中国科学技术大学）
- Shanghai University of Finance and Economics（上海财经大学）
- University of Milano-Bicocca（米兰比可卡大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出TabCon，一种基于表格基础模型的摊销推断系统，通过前向传播快速生成校准的置信区间，在基准数据集上实现接近标称覆盖率，且比经典自助法快50倍。
AI中文摘要：
几十年来，统计推断在很大程度上是逐个问题地发展的。给定一个科学目标，如处理效应或回归函数，统计学家会设计一个针对特定问题的估计量以及量化其不确定性的程序。本文提出了一种不同的范式。我们聚焦于统计推断中的一个经典问题——置信区间构建，并开发了TabCon，一个基于表格基础模型的摊销推断系统，通过简单的前向传播即可为新数据集生成置信区间。TabCon的关键方法成分包括稀疏混合专家架构和基于强化学习的后训练，这些将生成的置信区间校准到期望的覆盖率水平。在广泛的基准数据集上，TabCon达到了接近标称的覆盖率，同时生成了较短的置信区间。在推断时，它还提供了显著更高的计算效率，运行速度比经典自助法快50倍，即使后者仅使用50个自助样本。
英文摘要：
For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regression function, statisticians design a problem-specific estimator together with a procedure for quantifying its uncertainty. This paper proposes a different paradigm. We focus on a classical problem in statistical inference, confidence interval construction, and develop TabCon, an amortized inference system built on a tabular foundation model that produces confidence intervals for new datasets through a simple forward pass. The key methodological ingredients of TabCon are a sparse mixture-of-experts architecture and reinforcement-learning-based post-training that calibrate the resulting confidence intervals to a desired coverage level. Across a wide range of benchmark datasets, TabCon attains near-nominal coverage while producing short confidence intervals. At inference time, it also offers considerably greater computational efficiency, running 50 times faster than the classical bootstrap procedure, even when the latter uses only 50 bootstrap samples.
