2 papers
cs.LG2026
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2
Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statistical machine learning, partic…
eess.SY2025
Distributed Risk-Sensitive Safety Filters for Uncertain Discrete-Time Systems
Armin Lederer, Erfaun Noorani, Andreas Krause
Ensuring safety in multi-agent systems is a significant challenge, particularly in settings where centralized coordination is impractical. In this work, we propose a novel risk-sen…