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Guaranteeing safety in multi-agent systems with dynamic networks doesn't have to sacrifice performance: this plug-and-play protocol ensures recoverable safety even when agents join/leave or network topologies shift.
Achieve real-time online learning for model predictive control with a novel spatio-temporal Gaussian Process approximation that maintains constant computational complexity.
Ditch the conservative assumptions: this new uncertainty bound for kernel regression is distribution-free and scales to multi-output problems, all while playing nice with your existing Gaussian process workflows.
