Rashomon集的实用归因指导
Practical Attribution Guidance for Rashomon Sets
AI总结：
本文从实用角度研究Rashomon集采样问题，提出泛化性和实现稀疏性两个基本公理，并设计基于$ε$-次梯度的采样方法，通过数学问题和实际数据集验证其优于现有方法的能力。
摘要：
不同的预测模型在同一任务中可能表现同样出色（Rashomon集），但对数据提供相互冲突的解释和结论。可解释AI（XAI）背景下的Rashomon效应已被公认为关键因素。尽管Rashomon集已在多种情境中被引入和研究，但其实际应用仍处于初期阶段，缺乏足够的指导和评估。我们从实用角度研究Rashomon集采样问题，并识别出两个基本公理——泛化性和实现稀疏性，探索方法在实际使用中应满足这两个公理。大多数已知归因方法不满足这两个公理，我们认为这是一个根本性弱点。我们使用这些准则指导设计基于$ε$-次梯度的采样方法。我们将该方法应用于一个基本数学问题作为概念验证，并应用于一组实际数据集，以展示其与现有采样方法相比的能力。
英文摘要：
Different prediction models might perform equally well (Rashomon set) in the same task, but offer conflicting interpretations and conclusions about the data. The Rashomon effect in the context of Explainable AI (XAI) has been recognized as a critical factor. Although the Rashomon set has been introduced and studied in various contexts, its practical application is at its infancy stage and lacks adequate guidance and evaluation. We study the problem of the Rashomon set sampling from a practical viewpoint and identify two fundamental axioms - generalizability and implementation sparsity that exploring methods ought to satisfy in practical usage. These two axioms are not satisfied by most known attribution methods, which we consider to be a fundamental weakness. We use the norms to guide the design of an $ε$-subgradient-based sampling method. We apply this method to a fundamental mathematical problem as a proof of concept and to a set of practical datasets to demonstrate its ability compared with existing sampling methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
