论文标题
通过语法引导的合成，可能对机器学习模型进行了近似正确的解释
Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis
论文作者
论文摘要
我们提出了一种新的方法，可以使用可能近似正确的学习（PAC）和一种称为语法引导的合成（Sygus）的逻辑推理方法的组合来理解复杂的机器学习模型（例如，深神经网络）的决策。我们证明，我们的框架产生了解释，即以很高的可能性仅会造成很少的错误，并从经验上表明它有效地产生了人性化的小型解释。
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.
