Robust approachability and regret minimization in games with partial monitoring

Shie Mannor, Vianney Perchet, Gilles Stoltz

Research output: Contribution to journalConference articlepeer-review


Approachability has become a standard tool in analyzing learning algorithms in the adversarial online learning setup. We develop a variant of approachability for games where there is ambiguity in the obtained reward that belongs to a set, rather than being a single vector. Using this variant we tackle the problem of approachability in games with partial monitoring and develop simple and efficient algorithms (i.e., with constant per-step complexity) for this setup. We finally consider external and internal regret in repeated games with partial monitoring, for which we derive regret-minimizing strategies based on approachability theory.

Original languageEnglish
Pages (from-to)515-536
Number of pages22
JournalJournal of Machine Learning Research
StatePublished - 2011
Event24th International Conference on Learning Theory, COLT 2011 - Budapest, Hungary
Duration: 9 Jul 201111 Jul 2011


  • Adversarial learning
  • Approchability
  • Partial monitoring
  • Regret

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


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