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

• URL: http://arxiv.org/abs/2009.08770v1
• Date: Fri, 18 Sep 2020 12:10:49 GMT
• Title: Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis
• Authors: Daniel Neider and Bishwamittra Ghosh
• Abstract summary: 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.
• Score: 6.624726878647541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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