Safe Predictors for Enforcing Input-Output Specifications

Understand

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.