Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension. This is done using either a carefully designed projection or by a weight sharing technique. Via a novel uniﬁed analysis, we show that these two approaches deliver essentially equivalent bounds on a notion of regret generalizing shifting, adaptive, discounted, and other related regrets. Our analysis also captures and extends the generalized weight sharing technique of Bousquet and Warmuth, and can be reﬁned in several ways, including improvements for small losses and adaptive tuning of parameters.
|Titolo:||Mirror descent meets fixed share (and feels no regret)|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2012|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|