  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.
