Combinatorial online prediction via metarounding

Takahiro Fujita, Kohei Hatano, Eiji Takimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


We consider online prediction problems of combinatorial concepts. Examples of such concepts include s-t paths, permutations, truth assignments, set covers, and so on. The goal of the online prediction algorithm is to compete with the best fixed combinatorial concept in hindsight. A generic approach to this problem is to design an online prediction algorithm using the corresponding offline (approximation) algorithm as an oracle. The current state-of-the art method, however, is not efficient enough. In this paper we propose a more efficient online prediction algorithm when the offline approximation algorithm has a guarantee of the integrality gap.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 24th International Conference, ALT 2013, Proceedings
Number of pages15
Publication statusPublished - 2013
Event24th International Conference on Algorithmic Learning Theory, ALT 2013 - Singapore, Singapore
Duration: Oct 6 2013Oct 9 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8139 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other24th International Conference on Algorithmic Learning Theory, ALT 2013

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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