Data di Pubblicazione:
2017
Citazione:
Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback / N. Alon, N. Cesa Bianchi, C. Gentile, S. Mannor, Y. Mansour, O. Shamir. - In: SIAM JOURNAL ON COMPUTING. - ISSN 0097-5397. - 46:6(2017), pp. 1785-1826.
Abstract:
We introduce and study a partial-information model of online learning, where a decision maker repeatedly chooses from a finite set of actions, and observes some subset of the associated losses. This naturally models several situations where the losses of different actions are related, and knowing the loss of one action provides information on the loss of other actions. Moreover, it generalizes and interpolates between the well studied full-information setting (where all losses are revealed) and the bandit setting (where only the loss of the action chosen by the player is revealed). We provide several algorithms addressing different variants of our setting, and provide tight regret bounds depending on combinatorial properties of the information feedback structure.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
online learning; multi-armed bandits; learning from experts; learning with partial feedback; graph theory
Elenco autori:
N. Alon, N. Cesa Bianchi, C. Gentile, S. Mannor, Y. Mansour, O. Shamir
Link alla scheda completa:
Link al Full Text: