A distributionally robust PAC-Bayesian approach derives sub-Gaussian loss proxies and performance bounds tied to closed-loop operator norms via system level synthesis, enabling optimization-based safety certificates for controllers facing sim-to-real gaps.
Stein variational gradient descent: A general purpose bayesian inference algorithm
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A sampling-based safety filter using SV-MPC samples overrides unsafe nominal inputs and provides a probabilistic guarantee on restrictiveness via the scenario approach for collision avoidance.
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Distributionally Robust PAC-Bayesian Control
A distributionally robust PAC-Bayesian approach derives sub-Gaussian loss proxies and performance bounds tied to closed-loop operator norms via system level synthesis, enabling optimization-based safety certificates for controllers facing sim-to-real gaps.