Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration

NeurIPS 2019 Kwang-Sung JunAshok CutkoskyFrancesco Orabona

In this paper, we consider the nonparametric least square regression in a Reproducing Kernel Hilbert Space (RKHS). We propose a new randomized algorithm that has optimal generalization error bounds with respect to the square loss, closing a long-standing gap between upper and lower bounds... (read more)

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