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eess.SY2026
Goal-oriented safe active learning for predictive control using Bayesian recurrent neural networks
Laura Boca de Giuli, Alessio La Bella, Manish Prajapat +4
A key challenge in learning-based model predictive control (MPC) is to collect informative data online for model adaptation while ensuring safety and without penalising control per…
cs.LG2026
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Amon Lahr, Anna Scampicchio, Johannes Köhler +1
Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. I…