Optimistic Solutions for Dynamic Resource Allocation

In applications such as recommender systems, classical dynamic allocation rules are not a completely satisfying because they tend to propose always the same "blockbusters" and do not offer a sufficient variety of options. In this talk, I will present a model that, although inspired from real-time security analysis of a power system, can address this issue.
I will present an algorithm based on the optimistic paradigm and on the Good-Turing missing mass estimator. I will present two different regret
bounds on the performance of this algorithm under weak assumptions on the probabilistic experts. Under more restrictive hypotheses, a
macroscopic optimality result comparing the algorithm both with an oracle strategy and with uniform sampling, can be derived. Finally, I
will show numerical experiments illustrating these theoretical findings.