### Abstract

In this paper the sequential prediction problem with expert advice is considered when the loss is unbounded under partial monitoring scenarios. We deal with a wide class of the partial monitoring problems: the combination of the label efficient and multi-armed bandit problem, that is, where the algorithm is only informed about the performance of the chosen expert with probability ε ≤ 1. For bounded losses an algorithm is given whose expected regret scales with the square root of the loss of the best expert. For unbounded losses we prove that Hannan consistency can be achieved, depending on the growth rate of the average squared losses of the experts.

Original language | English |
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Title of host publication | Algorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings |

Publisher | Springer Verlag |

Pages | 229-243 |

Number of pages | 15 |

ISBN (Print) | 3540466495, 9783540466499 |

Publication status | Published - Jan 1 2006 |

Event | 17th International Conference on Algorithmic Learning Theory, ALT 2006 - Barcelona, Spain Duration: Oct 7 2006 → Oct 10 2006 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 4264 LNAI |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 17th International Conference on Algorithmic Learning Theory, ALT 2006 |
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Country | Spain |

City | Barcelona |

Period | 10/7/06 → 10/10/06 |

### Fingerprint

### ASJC Scopus subject areas

- Theoretical Computer Science
- Computer Science(all)

### Cite this

*Algorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings*(pp. 229-243). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4264 LNAI). Springer Verlag.