Active Learning for Approximation of Expensive Functions with Normal   Distributed Output Uncertainty
  When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy. We present the FLOLA-Voronoi method introduced previously for deterministic responses, and theoretically derive the impact of output uncertainty. The algorithm automatically puts more emphasis on exploration to provide more information to the models. 
Joachim van der Herten and Ivo Couckuyt and Dirk Deschrijver and Tom   Dhaene
cs.LG stat.ML