Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis

Daniel Neider; Bishwamittra Ghosh

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arXiv · 2009.08770

Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis

Daniel Neider·Bishwamittra Ghosh

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Abstract

We propose a novel approach to understanding the decision making of complex machine learning models (e.g., deep neural networks) using a combination of probably approximately correct learning (PAC) and a logic inference methodology called syntax-guided synthesis (SyGuS). We prove that our framework produces explanations that with a high probability make only few errors and show empirically that it is effective in generating small, human-interpretable explanations.

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Daniel Neider, Bishwamittra Ghosh. 2020-09-18. Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis. https://arxiv.org/abs/2009.08770

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