MaxHedge: Maximising a Maximum Online

Pasteris, Stephen; Vitale, Fabio; Chan, Kevin; Wang, Shiqiang; Herbster, Mark

Type de document :

Communication dans un congrès avec actes

Titre :

MaxHedge: Maximising a Maximum Online

Auteur(s) :

Pasteris, Stephen [Auteur]
Department of Computer science [University College of London] [UCL-CS]
Vitale, Fabio [Auteur]
Machine Learning in Information Networks [MAGNET]
Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome] [UNIROMA]
Chan, Kevin [Auteur]
U.S. Army Research Laboratory [Adelphi, MD] [ARL]
Wang, Shiqiang [Auteur]
Herbster, Mark [Auteur]
Department of Computer science [University College of London] [UCL-CS]

Titre de la manifestation scientifique :

International Conference on Artificial Intelligence and Statistics

Ville :

Naha, Okinawa

Pays :

Japon

Date de début de la manifestation scientifique :

2019-04-16

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. ...
Lire la suite >We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to maximise the cumulative profit, where the profit obtained on a single trial is defined as the difference between the maximum reward among the selected actions and the sum of their costs. Action energy values and the budget are known and fixed. All rewards and costs associated with each action change over time and are revealed at each trial only after the learner's selection of actions. Our framework encompasses several online learning problems where the environment changes over time; and the solution trades-off between minimising the costs and maximising the maximum reward of the selected subset of actions, while being constrained to an action energy budget. The algorithm that we propose is efficient and general that may be specialised to multiple natural online combinatorial problems .Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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https://hal.inria.fr/hal-02376987/document
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http://arxiv.org/pdf/1810.11843
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https://hal.inria.fr/hal-02376987/document
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https://hal.inria.fr/hal-02376987/document
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maxHedge.pdf
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1810.11843
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document
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MaxHedge: Maximising a Maximum Online

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