改进医学表格数据的不确定性估计，发现新方法反而不如原模型。
Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
- 将变分贝叶斯最后层与TabPFN结合，提升不确定性估计
- 在3个医学数据集上，原版TabPFN不确定性校准更优
- 挑战了轻量级方法提升不确定性的普遍认知
预测模型在医疗诊断等安全关键领域应用日益广泛，可靠不确定性估计至关重要。最近提出的表格先验拟合网络（TabPFN）是一种基于生成式Transformer架构的表格数据基础模型。变分贝叶斯最后层（VBLL）是一种先进的轻量级变分方法，能以极小计算开销有效提升不确定性估计。本文评估了将VBLL集成到最新提出的TabPFN中在不确定性校准方面的表现。实验在三个基准医学表格数据集上进行，对比了原始TabPFN与集成VBLL的版本。出乎意料的是，原始TabPFN在所有数据集上均持续优于集成VBLL的版本。
原文摘要 · Abstract (English)
Predictive models are being increasingly used across a wide range of domains, including safety-critical applications such as medical diagnosis and criminal justice. Reliable uncertainty estimation is a crucial task in such settings. Tabular Prior-data Fitted Network (TabPFN) is a recently proposed machine learning foundation model for tabular dataset, which uses a generative transformer architecture. Variational Bayesian Last Layers (VBLL) is a state-of-the-art lightweight variational formulation that effectively improves uncertainty estimation with minimal computational overhead. In this work we aim to evaluate the performance of VBLL integrated with the recently proposed TabPFN in uncertainty calibration. Our experiments, conducted on three benchmark medical tabular datasets, compare the performance of the original TabPFN and the VBLL-integrated version. Contrary to expectations, we observed that original TabPFN consistently outperforms VBLL integrated TabPFN in uncertainty calibration across all datasets.
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