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Oral
Learning deep kernels for exponential family densities
Li Kevin Wenliang · D.J. Sutherland · Heiko Strathmann · Arthur Gretton

Tue Jun 11 02:20 PM -- 02:25 PM (PDT) @ Room 101
in Kernel Methods »

The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching. Being required to choose a priori a simple kernel such as the Gaussian, however, limits its practical applicability. We provide a scheme for learning a kernel parameterized by a deep network, which can find complex location-dependent local features of the data geometry. This gives a very rich class of density models, capable of fitting complex structures on moderate-dimensional problems. Compared to deep density models fit via maximum likelihood, our approach provides a complementary set of strengths and tradeoffs: in empirical studies, the former can yield higher likelihoods, whereas the latter gives better estimates of the gradient of the log density, the score, which describes the distribution's shape.

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Author Information

Li Kevin Wenliang (Gatsby Unit, University College London)
D.J. Sutherland (Gatsby unit, University College London)
Heiko Strathmann (University College London)
Arthur Gretton (Gatsby Computational Neuroscience Unit)

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