[1/4] I will be talking about Measure Valued Derivatives for Approximate Bayesian Inference, our joint work with @elaClaudia @shakir_za @AndriyMnih, at the Bayesian Deep Learning workshop at 16:05 tomorrow.
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[2/4] Measure valued derivatives are a class of Monte Carlo gradient estimators that has been introduced 30 years ago by Georg Pflug, but is almost unknown in the machine learning community.
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[3/4] It has low variance, similar to the reparameterization gradients, and works with non-differentiable functions and discrete distributions, just like REINFORCE. The downside is the higher computational complexity that grows with the number of parameters.
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[4/4] We hope that this class of estimators will find exciting machine applications! The paper is available online at http://bayesiandeeplearning.org/2019/papers/76.pdf …
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