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- W1729157025 abstract "When dealing with time series with complex non-stationarities, low retrospective regret on individual realizations is a more appropriate goal than low prospective risk in expectation. Online learning algorithms provide powerful guarantees of this form, and have often been proposed for use with non-stationary processes because of their ability to switch between different forecasters or ``experts''. However, existing methods assume that the set of experts whose forecasts are to be combined are all given at the start, which is not plausible when dealing with a genuinely historical or evolutionary system. We show how to modify the ``fixed shares'' algorithm for tracking the best expert to cope with a steadily growing set of experts, obtained by fitting new models to new data as it becomes available, and obtain regret bounds for the growing ensemble." @default.
- W1729157025 created "2016-06-24" @default.
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- W1729157025 date "2011-03-04" @default.
- W1729157025 modified "2023-09-27" @default.
- W1729157025 title "Adapting to Non-stationarity with Growing Expert Ensembles" @default.
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