Systems and methods for training conditional generative models

Abstract

Systems and techniques for adjusting experiment parameters are illustrated. One embodiment includes a method that defines a joint distribution, wherein the joint distribution corresponds to a combination of a probabilistic model and a point prediction model, and wherein the point prediction model is configured to obtain a measurement of regression accuracy. The method derives an energy function for the joint distribution. The method obtains, from the energy function for the joint distribution, an approximation for a conditional distribution, wherein an output of the point prediction model is a parameter of the approximation. The method determines, from a loss function, at least one training parameter. The method trains the probabilistic based on the at least one parameter to operate as a conditional generative model, wherein the trained probabilistic model follows the conditional distribution. The method applies the trained probabilistic model to a dataset corresponding to a randomized trial.