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Description
There's been quite a bit of interesting work recently looking at natural gradients for variational inference with exponential family q-distributions, with non-conjugate / non-exponential family likelihoods / priors. See [1] (applied to GPs, but important bits aren't really GP-specific) and [2]. These turn out to be really quite straightforward to implement, so would be a great target for us. As a starting point, you could imagine extending our current mean field implementation to employ natural gradient descent in the parameters of the diagonal Gaussian q-distribution.
There's even work moving slightly beyond exponential family distributions now [3], but this is quite early work. Might be nice to have though.
[1] - Salimbeni, Hugh, Stefanos Eleftheriadis, and James Hensman. "Natural gradients in practice: Non-conjugate variational inference in Gaussian process models." arXiv preprint arXiv:1803.09151 (2018).
[2] - Khan, Mohammad Emtiyaz, and Didrik Nielsen. "Fast yet simple natural-gradient descent for variational inference in complex models." 2018 International Symposium on Information Theory and Its Applications (ISITA). IEEE, 2018.
[3] - Lin, Wu, Mohammad Emtiyaz Khan, and Mark Schmidt. "Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations." arXiv preprint arXiv:1906.02914 (2019).