Feb. 15, 2024, 5:41 a.m. | Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, Jose Miguel Hernandez-Lobato

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08845v1 Announce Type: new
Abstract: We investigate the problem of explainability in machine learning.To address this problem, Feature Attribution Methods (FAMs) measure the contribution of each feature through a perturbation test, where the difference in prediction is compared under different perturbations.However, such perturbation tests may not accurately distinguish the contributions of different features, when their change in prediction is the same after perturbation.In order to enhance the ability of FAMs to distinguish different features' contributions in this challenging setting, we …

abstract arxiv attribution cs.lg difference explainability feature machine machine learning prediction stage stat.me test tests through type via

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