Streaming Weak Submodularity: Interpreting Neural Networks on the Fly

Ethan R. Elenberg; Alexandros G. Dimakis; Moran Feldman; Amin Karbasi

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arXiv · 1703.02647

Streaming Weak Submodularity: Interpreting Neural Networks on the Fly

Ethan R. Elenberg·Alexandros G. Dimakis·Moran Feldman·Amin Karbasi

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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 $10$ times faster than the state-of-the-art LIME framework of Ribeiro et al. [2016].

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BibTeX RIS

Ethan R. Elenberg, Alexandros G. Dimakis, Moran Feldman, Amin Karbasi. 2017-11-22. Streaming Weak Submodularity: Interpreting Neural Networks on the Fly. https://arxiv.org/abs/1703.02647

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