Zeroth-order non-convex learning via hierarchical dual averaging

We propose a hierarchical version of dual averaging for zeroth-order online non-convex optimization - i.e., learning processes where, at each stage, the optimizer is facing an unknown non-convex loss function and only receives the incurred loss as feedback. The proposed class of policies relies on t...

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Hauptverfasser:	Héliou, Amélie, Martin, Matthieu, Mertikopoulos, Panayotis, Rahier, Thibaud
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Mathematics - Optimization and Control
Online-Zugang:	Volltext bestellen
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