Bayesian coresets
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If you have a question about this talk, please contact Isaac Reid.
Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders via lists.cam.ac.uk.
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 : http://arxiv.org/abs/1906.03329
This talk is part of the Machine Learning Reading Group @ CUED series.
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