Learning to Pivot with Adversarial Networks

Gilles Louppe
Michael Kagan
Kyle Cranmer

Many inference problems involve data generation processes that are not uniquely specified or are uncertain in some way. In a scientific context, the presence of several plausible data generation processes is often associated to the presence of systematic uncertainties. Robust inference is possible if it is based on a pivot -- a quantity whose distribution is invariant to the unknown value of the (categorical or continuous) nuisance parameters that parametrizes this family of generation processes. In this work, we introduce a flexible training procedure based on adversarial networks for enforcing the pivotal property on a predictive model. We derive theoretical results showing that the proposed algorithm tends towards a minimax solution corresponding to a predictive model that is both optimal and independent of the nuisance parameters (if that models exists) or for which one can tune the trade-off between power and robustness. Finally, we demonstrate the effectiveness of this approach with a toy example and an example from particle physics.

Please cite using the following BibTex entry:

@article{louppe2016pivot,
           author = {{Louppe}, G. and {Kagan}, M. and {Cranmer}, K.},
            title = "{Learning to Pivot with Adversarial Networks}",
          journal = {ArXiv e-prints},
    archivePrefix = "arXiv",
           eprint = {1611.01046},
     primaryClass = "stat.ML",
             year = 2016,
            month = nov,
}

SrGonao/paper-learning-to-pivot

Learning to Pivot with Adversarial Networks