Finite Sample Bounds for Non-Parametric Regression: Optimal Sample Efficiency and Space Complexity

ArXi:2412.14744v2 Announce Type: replace We address the problem of learning an unknown smooth function and its derivatives from noisy pointwise evaluations under the supremum norm. While classical nonparametric regression provides a strong theoretical foundation, traditional kernel-based estimators often incur high computational costs and memory requirements that scale with the sample size, limiting their utility in real-time applications such as reinforcement learning.