Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Abstract
In many machine learning applications, it is important to explain the predictions of a black-box classifier. For example, why does a deep neural network assign an image to a particular class? We cast interpretability of black-box classifiers as a combinatorial maximization problem and propose an efficient streaming algorithm to solve it subject to cardinality constraints. By extending ideas from Badanidiyuru et al. [2014], we provide a constant factor approximation guarantee for our algorithm in the case of random stream order and a weakly submodular objective function. This is the first such theoretical guarantee for this general class of functions, and we also show that no such algorithm exists for a worst case stream order. Our algorithm obtains similar explanations of Inception V3 predictions times faster than the state-of-the-art LIME framework of Ribeiro et al. [2016].
Cite
@article{arxiv.1703.02647,
title = {Streaming Weak Submodularity: Interpreting Neural Networks on the Fly},
author = {Ethan R. Elenberg and Alexandros G. Dimakis and Moran Feldman and Amin Karbasi},
journal= {arXiv preprint arXiv:1703.02647},
year = {2017}
}
Comments
To appear in NIPS 2017