<ul><li>Gaussian Process Regression (GPR) is a powerful method for learning complex functions from noisy data.</li><li>This paper introduces novel error bounds for GPR under bounded support noise.</li><li>The derived probabilistic and deterministic bounds are tighter than existing state-of-the-art bounds.</li><li>The bounds can be combined with stochastic barrier functions to quantify the safety probability of unknown dynamical systems.</li></ul>

Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification

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