1 citations · 1 across the 7 of their papers we have counts for
7 papers
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…
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…
Physics-informed learning under mixing: How physical knowledge speeds up learning
Anna Scampicchio, Leonardo F. Toso, Rahel Rickenbach +2
A major challenge in physics-informed machine learning is to understand how the incorporation of prior domain knowledge affects learning rates when data are dependent. Focusing on…
Optimal kernel regression bounds under energy-bounded noise
Amon Lahr, Johannes Köhler, Anna Scampicchio +1
Non-conservative uncertainty bounds are key for both assessing an estimation algorithm's accuracy and in view of downstream tasks, such as its deployment in safety-critical context…
Gaussian processes for dynamics learning in model predictive control
Anna Scampicchio, Elena Arcari, Amon Lahr +1
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dyn…
On the role of the signature transform in nonlinear systems and data-driven control
Anna Scampicchio, Melanie N. Zeilinger
Classic control techniques typically rely on a model of the system's response to external inputs, which is difficult to obtain from first principles especially if the unknown dynam…
On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics
Enea Monzio Compagnoni, Anna Scampicchio, Luca Biggio +3
Many finance, physics, and engineering phenomena are modeled by continuous-time dynamical systems driven by highly irregular (stochastic) inputs. A powerful tool to perform time se…