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arXiv · 2001.11062

Safe Predictors for Enforcing Input-Output Specifications

Stephen Mell·Olivia Brown·Justin Goodwin·Sung-Hyun Son
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Abstract

We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-output specifications before, during, and after algorithm training. Our method involves designing a constrained predictor for each set of compatible constraints, and combining them safely via a convex combination of their predictions. We demonstrate our approach on synthetic datasets and an aircraft collision avoidance problem.

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Stephen Mell, Olivia Brown, Justin Goodwin, Sung-Hyun Son. 2020-01-29. Safe Predictors for Enforcing Input-Output Specifications. https://arxiv.org/abs/2001.11062

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