A distributionally robust safety filter reduces certification for nonlinear systems under arbitrary uncertainties to a one-dimensional switching-time search with Wasserstein-inflated sampling guarantees.
Computing probabilistic controlled invariant sets
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Data-driven regularized least squares with self-normalized bounds and lattice abstraction yields certified (N, ε)-PCIS for linear MDPs via conservative backward recursion.
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Distributionally Robust Safety Under Arbitrary Uncertainties: A Safety Filtering Approach
A distributionally robust safety filter reduces certification for nonlinear systems under arbitrary uncertainties to a one-dimensional switching-time search with Wasserstein-inflated sampling guarantees.
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Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear MDPs
Data-driven regularized least squares with self-normalized bounds and lattice abstraction yields certified (N, ε)-PCIS for linear MDPs via conservative backward recursion.