AI RESEARCH
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
arXiv CS.LG
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ArXi:2605.09757v1 Announce Type: new 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 error bounds for kernel-based regression.