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Thompson Sampling for Stochastic Bandits with Graph Feedback

Aristide C. Y. Tossou (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers)) ; Christos Dimitrakakis (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers)) ; Devdatt Dubhashi (Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers))
AAAI 2017 p. 2660-2666. (2017)
[Konferensbidrag, refereegranskat]

We present a novel extension of Thompson Sampling for stochastic sequential decision problems with graph feedback, even when the graph structure itself is unknown and/or changing. We provide theoretical guarantees on the Bayesian regret of the algorithm, linking its performance to the underlying properties of the graph. Thompson Sampling has the advantage of being applicable without the need to construct complicated upper confidence bounds for different problems. We illustrate its performance through extensive experimental results on real and simulated networks with graph feedback. More specifically, we tested our algorithms on power law, planted partitions and Erdo ̋s–Renyi graphs, as well as on graphs derived from Facebook and Flixster data. These all show that our algorithms clearly outperform related methods that employ upper confidence bounds, even if the latter use more information about the graph.



Denna post skapades 2017-10-09.
CPL Pubid: 252409

 

Institutioner (Chalmers)

Institutionen för data- och informationsteknik, Datavetenskap, Algoritmer (Chalmers)

Ämnesområden

Datalogi

Chalmers infrastruktur

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