首个可解释的表格基础模型，用单次前向传播实现快速预测。
GAMformer: Bridging Tabular Foundation Models and Interpretable Machine Learning
- 基于上下文学习，单次前向计算估计可解释函数形状。
- 在多个分类基准上性能媲美主流GAM方法。
- 仅用合成数据训练，避免真实数据泄露，适合高安全场景。
尽管可解释性对安全关键领域和合规性至关重要，现有表格基础模型（如TabPFN）缺乏透明性。广义加性模型（GAM）通过其可加结构提供所需可解释性，但传统GAM方法依赖迭代学习算法（如样条、提升树或神经网络），与基础模型的上下文学习范式不兼容。本文提出GAMformer，首个面向GAM的表格基础模型，弥合了基础模型能力与可解释性需求之间的差距。GAMformer通过上下文学习在单次前向传播中估算GAM形状函数，显著区别于传统迭代方法。基于先前表格基础模型研究，我们仅使用合成生成表格进行训练，以防止数据泄露。实验表明，GAMformer在多个分类基准上表现媲美领先的GAM方法。
原文摘要 · Abstract (English)
While interpretability is crucial for machine learning applications in safety-critical domains and for regulatory compliance, existing tabular foundation models like TabPFN lack transparency. Generalized Additive Models (GAMs) provide the needed interpretability through their additive structure, but traditional GAM methods rely on iterative learning algorithms (such as splines, boosted trees, or neural networks) that are fundamentally incompatible with the in-context learning paradigm of foundation models. In this paper, we introduce GAMformer, the first tabular foundation model for GAMs that bridges the gap between the power of foundation models and the interpretability requirements of critical real-world applications. GAMformer estimates GAM shape functions in a single forward pass using in-context learning, representing a significant departure from conventional iterative approaches. Building on previous research on tabular foundation models, we train GAMformer exclusively on synthetically generated tables to prevent data leakage. Our experiments demonstrate that GAMformer performs comparably to other leading GAMs across various classification benchmarks.
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