Online combinatorial optimization with stochastic decision sets and adversarial losses - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Online combinatorial optimization with stochastic decision sets and adversarial losses

Gergely Neu
  • Fonction : Auteur
  • PersonId : 961171
Michal Valko

Résumé

Most work on sequential learning assumes a fixed set of actions that are available all the time. However, in practice, actions can consist of picking subsets of readings from sensors that may break from time to time, road segments that can be blocked or goods that are out of stock. In this paper we study learning algorithms that are able to deal with stochastic availability of such unreliable composite actions. We propose and analyze algorithms based on the Follow-The-Perturbed-Leader prediction method for several learning settings differing in the feedback provided to the learner. Our algorithms rely on a novel loss estimation technique that we call Counting Asleep Times. We deliver regret bounds for our algorithms for the previously studied full information and (semi-)bandit settings, as well as a natural middle point between the two that we call the restricted information setting. A special consequence of our results is a significant improvement of the best known performance guarantees achieved by an efficient algorithm for the sleeping bandit problem with stochastic availability. Finally, we evaluate our algorithms empirically and show their improvement over the known approaches.
Fichier principal
Vignette du fichier
neu2014online.pdf (469.89 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01079355 , version 1 (01-11-2014)
hal-01079355 , version 2 (03-11-2014)

Identifiants

  • HAL Id : hal-01079355 , version 2

Citer

Gergely Neu, Michal Valko. Online combinatorial optimization with stochastic decision sets and adversarial losses. Neural Information Processing Systems, Dec 2014, Montréal, Canada. ⟨hal-01079355v2⟩
154 Consultations
201 Téléchargements

Partager

Gmail Facebook X LinkedIn More