Stochastic simultaneous optimistic optimization

ArXi:2604.24537v1 Announce Type: new We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locally smooth (in some precise sense) with respect to some semi-metric, around one of its global maxima. Compared to previous works on bandits in general spaces (Kleinberg, 2008; Bubeck, 2011a) our algorithm does not require the knowledge of this semi-metric.