A tailored scenario optimization method yields tunable reachable-set estimates with a posteriori guarantees against adversarial noise and explicit degradation bounds under Wasserstein shifts.
Data-driven forward stochastic reachability analysis for human-in-the-loop systems,
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Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees
A tailored scenario optimization method yields tunable reachable-set estimates with a posteriori guarantees against adversarial noise and explicit degradation bounds under Wasserstein shifts.