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Optimal discovery with probabilistic expert advice
Optimal discovery with probabilistic expert advice
We consider an original problem that arises from the issue of security
analysis of a power system and that we name optimal discovery with
probabilistic expert advice. We address it with an algorithm based on
the optimistic paradigm and the Good-Turing missing mass estimator. We
show that this strategy uniformly attains the optimal discovery rate in
a macroscopic limit sense, under some assumptions on the probabilistic
experts. We also provide numerical experiments suggesting that this
optimal behavior may still hold under weaker assumptions.
