MaxHedge: Maximising a Maximum Online

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

Document type :

Communication dans un congrès avec actes

Title :

MaxHedge: Maximising a Maximum Online

Author(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]

Conference title :

International Conference on Artificial Intelligence and Statistics

City :

Naha, Okinawa

Country :

Japon

Start date of the conference :

2019-04-16

HAL domain(s) :

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

English abstract : [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. ...
Show more >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 .Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL

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