Edit Paper: Efficient local validation of partially ordered models via Baysian directed sampling

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Title*:	Efficient local validation of partially ordered models via Baysian directed sampling
Authors*:	Kellan Moorse and Richard Murray
Source*:	Submitted, 2024 American Control Conference (ACC)
Abstract*:	We consider the problem of estimating the subset of test conditions under which a simplified model—or set of simplified models—accurately approximates the behavior of a true system. We approach the problem by proposing a compact set of possible test conditions, and an unknown but samplable continous validity function over that set that quantifies the accuracy of the model under each possible condition. We propose a novel Bayes estimator that optimally directs function sampling to greedily minimize the expected posterior misclassification rate of the valid set, which we call minimum posterior misclassification sampling (GP-MPM), and we show that the the method can be extended to approximate the valid sets of a partially ordered set of models, with sample complexity growing sublinearly with the number of models. In testing against a safety-focused model, we show that the algorithm’s estimated valid set approaches the true valid set much more quickly than undirected sampling, even with small sample sizes.
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