  The paper focuses on the a posteriori tuning of a generative model in order
to favor the generation of good instances in the sense of some external
differentiable criterion. The proposed approach, called Boltzmann Tuning of
Generative Models (BTGM), applies to a wide range of applications. It covers
conditional generative modelling as a particular case, and offers an affordable
alternative to rejection sampling. The contribution of the paper is twofold.
Firstly, the objective is formalized and tackled as a well-posed optimization
problem; a practical methodology is proposed to choose among the candidate
criteria representing the same goal, the one best suited to efficiently learn a
tuned generative model. Secondly, the merits of the approach are demonstrated
on a real-world application, in the context of robust design for energy
policies, showing the ability of BTGM to sample the extreme regions of the
considered criteria.
