Recognition: unknown
Prediction with Advice of Unknown Number of Experts
classification
💻 cs.LG
stat.ML
keywords
expertsnumbernominaladviceboundboundsdefensivedepends
read the original abstract
In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the Normal- Hedge bound, which mainly depends on the effective number of experts but also weakly depends on the nominal one, we obtain a bound that does not contain the nominal number of experts at all. We use the defensive forecasting method and introduce an application of defensive forecasting to multivalued supermartingales.
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Cited by 1 Pith paper
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A Note on How to Remove the $\ln\ln T$ Term from the Squint Bound
Shifted KT potentials equal a prior change in KT, and this removes the ln ln T factor from Squint's data-independent bound.
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