On the Pinsker bound of inner product kernel regression in large dimensions
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Abstract (arXiv)
Building on recent studies of large-dimensional kernel regression, particularly those involving inner product kernels on the sphere mathbbSd, we investigate the Pinsker bound for inner product kernel regression in such settings. Specifically, we address the scenario where the sample size n is given by α dγ(1+od(1)) for some α, γ>0. We have determined the exact minimax risk for kernel regression in this setting, not only identifying the minimax rate but also the exact constant, known as the Pinsker constant, associated with the excess risk.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 43 of its 79 references that are in the map. First posted on arXiv 2 Sep 2024.
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31 more
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