2026
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
ICML 2026poster
Providing non-conservative uncertainty quantification for function estimates derived from noisy observations remains a fundamental challenge in statistical machine learning, particularly for applications in safety-critical domains. In this work, we propose novel non-asymptotic probabilistic uniform …