Putting Bayes to sleep

NeurIPS 2012 Dmitry AdamskiyManfred K. WarmuthWouter M. Koolen

We consider sequential prediction algorithms that are given the predictions from a set of models as inputs. If the nature of the data is changing over time in that different models predict well on different segments of the data, then adaptivity is typically achieved by mixing into the weights in each round a bit of the initial prior (kind of like a weak restart)... (read more)

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