arXiv Open Access 2020

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

Daniel Neider Bishwamittra Ghosh

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Abstrak

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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cs.AI

Penulis (2)

Daniel Neider

Bishwamittra Ghosh

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Neider, D., Ghosh, B. (2020). Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis. https://arxiv.org/abs/2009.08770

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Tahun Terbit: 2020
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓