Scale-free Unconstrained Online Learning for Curved Losses

A sequence of works in unconstrained online convex optimisation have investigated the possibility of adapting simultaneously to the norm \(U\) of the comparator and the maximum norm \(G\) of the gradients. In full generality, matching upper and lower bounds are known which show that this comes at th...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	arXiv.org 2022-06
Hauptverfasser:	Mayo, Jack J, Hadiji, Hédi, Tim van Erven
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive algorithms Distance learning Least squares method Lower bounds Machine learning Matching Optimization Regression Rescaling
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!