  Several techniques for domain adaptation have been proposed to account for
differences in the distribution of the data used for training and testing. The
majority of this work focuses on a binary domain label. Similar problems occur
in a scientific context where there may be a continuous family of plausible
data generation processes associated to the presence of systematic
uncertainties. Robust inference is possible if it is based on a pivot -- a
quantity whose distribution does not depend on the unknown values of the
nuisance parameters that parametrize this family of data generation processes.
In this work, we introduce and derive theoretical results for a training
procedure based on adversarial networks for enforcing the pivotal property (or,
equivalently, fairness with respect to continuous attributes) on a predictive
model. The method includes a hyperparameter to control the trade-off between
accuracy and robustness. We demonstrate the effectiveness of this approach with
a toy example and examples from particle physics.

Machine Learning;Machine Learning;Neural and Evolutionary Computing;Data Analysis, Statistics and Probability;Methodology