We propose a fast method with statistical guarantees for learning an exponential family density model where the natural parameter is in a reproducing kernel Hilbert space, and may be infinite dimensional. The model is learned by fitting the derivative of the log density, the score, thus avoiding the need to compute a normalization constant. We improved the computational efficiency of an earlier solution with a low-rank, Nystr\"om-like solution. The new solution remains consistent, and is shown to converge in Fisher distance at the same rate as a full-rank solution, with guarantees on the degree of cost and storage reduction. We compare to a popular score learning approach using a denoising autoencoder, in experiments on density estimation and in the construction of an adaptive Hamiltonian Monte Carlo sampler. Apart from the lack of statistical guarantees for the autoencoder, our estimator is more data-efficient when estimating the score, runs faster, and has fewer parameters (which can be tuned in a principled and interpretable way).
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