发表机构
ML Analytics; Delft University of Technology; King’s College London; European Space Agency（ML Analytics; 代尔夫特理工大学; 伦敦国王学院; 欧洲空间局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对Ariel任务的光谱数据归因与误差评估需求，提出基于影响函数的可追溯谱推断方法，通过极限学习机实现高效计算，其误差代理可准确关联光谱误差，为科学机器学习提供运行框架。
AI 中文摘要
可解释性对于部署在ESA的Ariel等科学空间任务中的机器学习模型至关重要，这些任务在运行期间无法获得真实值，必须评估物理合理性。大多数可解释AI方法聚焦于特征归因，本研究通过影响函数探究训练数据归因，并为运行光谱管道引入三项关键贡献：一是将影响重新表述为预测而非损失，实现无标签部署；二是利用极限学习机（Extreme Learning Machine）的闭式岭解，高效计算无穷小预测影响；三是通过影响敏感性传播训练残差，推导基于影响的保守误差代理。在模拟光谱上的评估显示，所提代理与基于尺度和形状的光谱误差具有强相关性，此外影响函数可识别最具影响力的样本并近似最有害的样本。这些结果共同表明，该方法可作为科学机器学习的运行框架。
英文摘要
Interpretability is critical for machine learning models deployed in scientific space missions such as ESA's Ariel, where ground truth is unavailable during operations and physical plausibility must be assessed. While most explainable AI methods focus on feature attribution, this work investigates training data attribution through influence functions and introduces three key contributions for operational spectroscopy pipelines. First, influence is reformulated in terms of prediction rather than loss, enabling label-free deployment. Second, by leveraging the closed-form ridge solution of an Extreme Learning Machine, infinitesimal prediction influence is efficiently computed. Third, an influence-based conservative error proxy is derived by propagating training residuals through the influence sensitivities. Evaluated against simulated spectra, the proposed proxy correlates strongly with scale and shape-based spectral errors. Furthermore, influence functions enable the identification of the most influential samples and the approximation of the most harmful ones. Together, these results suggest that this approach can serve as an operational framework for scientific machine learning.
CommentsTo appear in "Proceedings of SPAICE 2026: Third Conference on AI in and for Space"
