Safe Predictors for Enforcing Input-Output Specifications

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.

10 pages, 5 figures, paper accepted to the NeurIPS 2019 Workshop on Machine Learning with Guarantees and the NeurIPS 2019 Workshop on Safety and Robustness in Decision Making

References in corpus (2)

• Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
• Towards Proving the Adversarial Robustness of Deep Neural Networks

Cited by in corpus (1)

• Safety Aware Reinforcement Learning (SARL)