Developing Bug-Free Machine Learning Systems With Formal Mathematics

Abstract

Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proof assistant to both implement their system and to state a… (More)

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4 Figures and Tables

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