Online learning with noisy side observations

Kocák, Tomáš; Neu, Gergely; Valko, Michal

Document type :

Communication dans un congrès avec actes

Title :

Online learning with noisy side observations

Author(s) :

Kocák, Tomáš [Auteur]
Sequential Learning [SEQUEL]
Neu, Gergely [Auteur]
Sequential Learning [SEQUEL]
Universitat Pompeu Fabra [Barcelona] [UPF]
Valko, Michal [Auteur]

Sequential Learning [SEQUEL]

Conference title :

International Conference on Artificial Intelligence and Statistics

City :

Seville

Country :

Espagne

Start date of the conference :

2016-05-09

HAL domain(s) :

Statistiques [stat]/Machine Learning [stat.ML]

English abstract : [en]

We propose a new partial-observability model for online learning problems where the learner, besides its own loss, also observes some noisy feedback about the other actions, depending on the underlying structure of the ...
Show more >We propose a new partial-observability model for online learning problems where the learner, besides its own loss, also observes some noisy feedback about the other actions, depending on the underlying structure of the problem. We represent this structure by a weighted directed graph, where the edge weights are related to the quality of the feedback shared by the connected nodes. Our main contribution is an efficient algorithm that guarantees a regret of O(√ α * T) after T rounds, where α * is a novel graph property that we call the effective independence number. Our algorithm is completely parameter-free and does not require knowledge (or even estimation) of α *. For the special case of binary edge weights, our setting reduces to the partial-observability models of Mannor & Shamir (2011) and Alon et al. (2013) and our algorithm recovers the near-optimal regret bounds.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Extraction et transfert de connaissances dans l'apprentissage par renforcement

Collections :