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Robust approachability and regret minimization in games with partial monitoring
This video was recorded at 24th Annual Conference on Learning Theory (COLT), Budapest 2011. 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 regretminimizing strategies based on approachability theory.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
