%0 Journal Article %1 salimbeni2018natural %A Salimbeni, Hugh %A Eleftheriadis, Stefanos %A Hensman, James %D 2018 %K gaussian-proceses optimization readings variational %T Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models %U http://arxiv.org/abs/1803.09151 %X The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients can be used in non-conjugate stochastic settings, together with hyperparameter learning. We conclude that the natural gradient can significantly improve performance in terms of wall-clock time. For ill-conditioned posteriors the benefit of the natural gradient method is especially pronounced, and we demonstrate a practical setting where ordinary gradients are unusable. We show how natural gradients can be computed efficiently and automatically in any parameterization, using automatic differentiation. Our code is integrated into the GPflow package.

@article{salimbeni2018natural, abstract = {The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored. We examine how natural gradients can be used in non-conjugate stochastic settings, together with hyperparameter learning. We conclude that the natural gradient can significantly improve performance in terms of wall-clock time. For ill-conditioned posteriors the benefit of the natural gradient method is especially pronounced, and we demonstrate a practical setting where ordinary gradients are unusable. We show how natural gradients can be computed efficiently and automatically in any parameterization, using automatic differentiation. Our code is integrated into the GPflow package.}, added-at = {2020-02-24T04:36:22.000+0100}, author = {Salimbeni, Hugh and Eleftheriadis, Stefanos and Hensman, James}, biburl = {https://www.bibsonomy.org/bibtex/290161205624adc0b1c93fb264fa39b23/kirk86}, description = {[1803.09151] Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models}, interhash = {3972b85e1f456cd4aa3f482c4f95ac4e}, intrahash = {90161205624adc0b1c93fb264fa39b23}, keywords = {gaussian-proceses optimization readings variational}, note = {cite arxiv:1803.09151}, timestamp = {2020-02-24T04:36:22.000+0100}, title = {Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models}, url = {http://arxiv.org/abs/1803.09151}, year = 2018 }