General parallel optimization a without metric


Xuedong Shang, Emilie Kaufmann, Michal Valko ;
Proceedings of the 30th International Conference on Algorithmic Learning Theory, PMLR 98:762-788, 2019.


Hierarchical bandits are an approach for global optimization of \emph{extremely} irregular functions. This paper provides new elements regarding POO, an adaptive meta-algorithm that does not require the knowledge of local smoothness of the target function. We first highlight the fact that the subroutine algorithm used in POO should have a small regret under the assumption of \emph{local smoothness with respect to the chosen partitioning}, which is unknown if it is satisfied by the standard subroutine HOO. In this work, we establish such regret guarantee for HCT, which is another hierarchical optimistic optimization algorithm that needs to know the smoothness. This confirms the validity of POO. We show that POO can be used with HCT as a subroutine with a regret upper bound that matches the one of best-known algorithms using the knowledge of smoothness up to a $\sqrt{\log{n}}$ factor. On top of that, we further propose a more general wrapper, called GPO, that can cope with algorithms that only have simple regret guarantees.

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