%0 Journal Article %1 wenliang2018learning %A Wenliang, Li %A Sutherland, Dougal %A Strathmann, Heiko %A Gretton, Arthur %D 2018 %K bayesian kernels probability readings stats %T Learning deep kernels for exponential family densities %U http://arxiv.org/abs/1811.08357 %X 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.

@article{wenliang2018learning, abstract = {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.}, added-at = {2020-02-12T21:41:26.000+0100}, author = {Wenliang, Li and Sutherland, Dougal and Strathmann, Heiko and Gretton, Arthur}, biburl = {https://www.bibsonomy.org/bibtex/27c538f0c5004df4cbf401954503528dc/kirk86}, description = {[1811.08357] Learning deep kernels for exponential family densities}, interhash = {ada37bddec0d1d7a74b364f548ab3054}, intrahash = {7c538f0c5004df4cbf401954503528dc}, keywords = {bayesian kernels probability readings stats}, note = {cite arxiv:1811.08357}, timestamp = {2020-02-12T21:41:26.000+0100}, title = {Learning deep kernels for exponential family densities}, url = {http://arxiv.org/abs/1811.08357}, year = 2018 }