  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 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.
