A Learning Theoretic Perspective on Local ExplainabilityDownload PDF

28 Sept 2020, 15:50 (modified: 18 Mar 2021, 11:49)ICLR 2021 PosterReaders: Everyone
Keywords: Interpretability, Learning Theory, Local Explanations, Generalization
Abstract: In this paper, we explore connections between interpretable machine learning and learning theory through the lens of local approximation explanations. First, we tackle the traditional problem of performance generalization and bound the test-time predictive accuracy of a model using a notion of how locally explainable it is. Second, we explore the novel problem of explanation generalization which is an important concern for a growing class of finite sample-based local approximation explanations. Finally, we validate our theoretical results empirically and show that they reflect what can be seen in practice.
Supplementary Material: zip
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
8 Replies

Loading