Provably Better Explanations with Optimized Aggregation of Feature Attributions
LMU Munich（慕尼黑大学）；Siemens AG（西门子公司）；Technical University of Munich（慕尼黑工业大学）；University of Oxford（牛津大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Goethe University Frankfurt（法兰克福大学）
Thomas Decker, Ananta R. Bhattarai, Jindong Gu, Volker Tresp, Florian Buettner
Using feature attributions for post-hoc explanations is a common practice to understand and verify the predictions of opaque machine learning models. Despite the numerous techniques available, individual methods often produce inconsistent and unstable results, putting their overall reliability into question. In this work, we aim to systematically improve the quality of feature attributions by combining multiple explanations across distinct methods or their variations. For this purpose, we propose a novel approach to derive optimal convex combinations of feature attributions that yield provable improvements of desired quality criteria such as robustness or faithfulness to the model behavior. Through extensive experiments involving various model architectures and popular feature attribution techniques, we demonstrate that our combination strategy consistently outperforms individual methods and existing baselines.
本文研究了图神经网络（GNNs）的忠实解释。首先，我们提供了一个新的和一般的方法来正式表征GNN的解释的忠实性。它适用于现有的解释方法，包括特征属性和子图解释。其次，我们的分析和实证结果表明，特征属性的方法不能捕捉边缘特征的非线性效应，而现有的子图解释方法是不忠实的。第三，我们介绍了\emph{k-hop Explanation with a Convolutional Core}（KEC），一种新的解释方法，可证明最大限度地忠实于原来的GNN利用信息的图结构在其邻接矩阵和它的\emph{k-th}权力。最后，我们对使用GNN进行分类和异常检测任务的合成和真实数据集的实证结果证明了我们方法的有效性。
A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty
Leibniz Institute for Prevention Research and Epidemiology – BIPS（莱布尼茨预防研究与流行病学研究所——BIPS）；University of Bremen（不来梅大学）；Simula Research Laboratory（西穆拉研究实验室）；Telenor Research & Innovation（挪威电信研究与创新部）；Norwegian Computing Center（挪威计算中心）
