1 citations · 1 across the 7 of their papers we have counts for
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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…
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…