Integral Transforms, Reproducing Kernels and Their Applications

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Kernel Adaptive Metropolis-Hastings (2013, background): “ance function κof this GP is therefore the kernel kconvolved with itself with respect to the empirical measure associated to the samples z, and draws from this GP therefore lie in a smaller RKHS; see Saitoh (1997, p. 21) for details. Obtaining Target Samples through Gradient Descent. We have seen ho”
Quadratic Fourier Transforms (2014, methodology): “4) is given by the methods of the restriction of some tensor product and sum of reproducing kernels, and so its structure is very complicated (see [20]).”
Input-dependent estimation of generalization error under covariate shift (2005, background): “If the functional Hilbert space H has the reproducing kernel [3, 30, 50, 31, 49, 32], J = 0 if and only if f̂(x) = f(x) for all x in D.”