Efficient and principled score estimation with Nyström kernel exponential families

@inproceedings{Sutherland2018EfficientAP,
  title={Efficient and principled score estimation with Nystr{\"o}m kernel exponential families},
  author={Dougal J. Sutherland and Heiko Strathmann and Michael Arbel and Arthur Gretton},
  booktitle={AISTATS},
  year={2018}
}
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. Our approach improves the computational efficiency of an earlier solution by using a low-rank, Nyströmlike solution. The new solution retains the… CONTINUE READING
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