BAMS combines Gaussian-process-based adaptive sampling with low- and high-fidelity simulators to discover rare AV failures and estimate their rate more efficiently than Monte Carlo and importance-sampling baselines.
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Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling
BAMS combines Gaussian-process-based adaptive sampling with low- and high-fidelity simulators to discover rare AV failures and estimate their rate more efficiently than Monte Carlo and importance-sampling baselines.