现有预条件器能否提升生物医学表格基础学习？TabPFN优化实证研究
原标题：Do Existing Preconditioners Improve Biomedical Tabular Foundation Learning? An Empirical Study on TabPFN Optimization
AI 摘要
该研究对TabPFN v2.5在59个生物医学数据集上的微调进行了实证研究，比较了五种基于AdamW的预条件优化策略。结果显示，原始AdamW优化器在预测性能和统计排名上始终最佳，而现有的曲率感知预条件器未能带来可靠改进。作者认为通用预条件方法可能无法充分捕捉生物医学表格学习的优化特性，需开发面向医疗的专用预条件器。
正文节选
Do Existing Preconditioners Improve Biomedical Tabular Foundation Learning? An Empirical Study on TabPFN Optimization Abstract Tabular foundation models have recently shown strong potential for structured biomedical data analysis. Among them, TabPFN has emerged as an effective approach for low-data tabular classification tasks. However, the impact of optimization and preconditioning strategies on biomedical fine-tuning remains largely unexplored. In this work, we present a comprehensive empirica