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Bayesian coresets

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The proliferation of automated inference algorithms in Bayesian statistics has provided practitioners access to fast, reproducible data analysis and powerful statistical models. But designing automated methods that are both scalable in the number of observations in addition to theoretically sound is still a challenging task.

In this talk we’ll discuss a family of recent works trying to address this referred to as Bayesian coresets. We’ll discuss the basic idea behind Bayesian coresets, and then look at some of the literature together.

Recomended reading: Campbell & Beronov (2019) Sparse Variational Inference: Bayesian Coresets From Scratch. URL :

This talk is part of the Machine Learning Reading Group @ CUED series.

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