An Epistemic Approach to the Formal Specification of Statistical Machine Learning

• URL: http://arxiv.org/abs/2004.12734v3
• Date: Sun, 20 Sep 2020 17:51:14 GMT
• Title: An Epistemic Approach to the Formal Specification of Statistical Machine Learning
• Authors: Yusuke Kawamoto
• Abstract summary: We introduce a formal model for supervised learning based on a Kripke model. We then formalize various notions of the classification performance, robustness, and fairness of statistical classifiers.
• Score: 1.599072005190786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an epistemic approach to formalizing statistical properties of machine learning. Specifically, we introduce a formal model for supervised learning based on a Kripke model where each possible world corresponds to a possible dataset and modal operators are interpreted as transformation and testing on datasets. Then we formalize various notions of the classification performance, robustness, and fairness of statistical classifiers by using our extension of statistical epistemic logic (StatEL). In this formalization, we show relationships among properties of classifiers, and relevance between classification performance and robustness. As far as we know, this is the first work that uses epistemic models and logical formulas to express statistical properties of machine learning, and would be a starting point to develop theories of formal specification of machine learning.

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