Cooperative Online Learning with Feedback Graphs

Fuente: arXiv
Guardado en:
Detalles Bibliográficos
Autores principales: Cesa-Bianchi, Nicolò, Cesari, Tommaso R., Della Vecchia, Riccardo
Formato: Preprint
Publicado: 2021
Materias:
Acceso en línea:
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
_version_ 1866910561823883264
author Cesa-Bianchi, Nicolò
Cesari, Tommaso R.
Della Vecchia, Riccardo
author_facet Cesa-Bianchi, Nicolò
Cesari, Tommaso R.
Della Vecchia, Riccardo
contents We study the interplay between communication and feedback in a cooperative online learning setting, where a network of communicating agents learn a common sequential decision-making task through a feedback graph. We bound the network regret in terms of the independence number of the strong product between the communication network and the feedback graph. Our analysis recovers as special cases many previously known bounds for cooperative online learning with expert or bandit feedback. We also prove an instance-based lower bound, demonstrating that our positive results are not improvable except in pathological cases. Experiments on synthetic data confirm our theoretical findings.
format Preprint
id arxiv_https___arxiv_org_abs_2106_04982
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Cooperative Online Learning with Feedback Graphs
Cesa-Bianchi, Nicolò
Cesari, Tommaso R.
Della Vecchia, Riccardo
Machine Learning
We study the interplay between communication and feedback in a cooperative online learning setting, where a network of communicating agents learn a common sequential decision-making task through a feedback graph. We bound the network regret in terms of the independence number of the strong product between the communication network and the feedback graph. Our analysis recovers as special cases many previously known bounds for cooperative online learning with expert or bandit feedback. We also prove an instance-based lower bound, demonstrating that our positive results are not improvable except in pathological cases. Experiments on synthetic data confirm our theoretical findings.
title Cooperative Online Learning with Feedback Graphs
topic Machine Learning
url https://arxiv.org/abs/2106.04982