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Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Ethan Elenberg · Alexandros Dimakis · Moran Feldman · Amin Karbasi

Tue Dec 06:30 PM -- 10:30 PM PST @ Pacific Ballroom #155 #None

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].

Author Information

Ethan Elenberg (ASAPP)
Alex Dimakis (University of Texas, Austin)
Moran Feldman (Open University of Israel)
Amin Karbasi (Yale)

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