Appendix

This is the appendix of the paper "Quantification of Uncertainty with Adversarial Models". It consists of three sections. In view of the increasing influence of contemporary machine learning research on the broader public, section A gives a societal impact statement. Following to this, section B gives details of our theoretical results, foremost about the measure of uncertainty used throughout our work. Furthermore, Mixture Importance Sampling for variance reduction is discussed. Finally, section C gives details about the experiments presented in the main paper, as well as further experiments. In this work, we have focused on improving the predictive uncertainty estimation for machine learning models, specifically deep learning models. Our primary goal is to enhance the robustness and reliability of these predictions, which we believe have several positive societal impacts. This could have implications across various sectors, including healthcare, finance, and autonomous vehicles, where decision-making based on machine learning predictions can directly affect human lives and economic stability. This could foster greater acceptance and integration of machine learning technologies in everyday life, driving societal advancement. By advancing this area, our work promotes the use of those methods in an ethical, transparent, and accountable manner.