Learning to Pivot with Adversarial Networks
This addresses robust inference in scientific contexts with continuous nuisance parameters, representing an incremental advance in domain adaptation techniques.
The paper tackles the problem of domain adaptation for continuous systematic uncertainties by introducing a training procedure using adversarial networks to enforce pivotal properties on predictive models, demonstrating effectiveness with a toy example and particle physics applications.
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.