However, in practice, each datapoint requires an expensive call to a numerical simulator. Therefore, we need to use active learning techniques or reinforcement learning-based strategies to optimally sample the initial dataset and re-use the simulator judiciously during the optimization phase.
An efficient active learning strategy to take the best advantage of a limited number of calls to the simulator before and during the optimization process would have a strong theoretical and industrial impact.
Computer Vision Laboratory, EPFL, Lausanne.
12 Weeks or more, anytime.
Please contact us pierre.baque(at)epfl.ch or edoardo.remelli(at)epfl.ch for any further information.