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Poster
Learning to Pivot with Adversarial Networks
Gilles Louppe · Michael Kagan · Kyle Cranmer

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #105 #None

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.

Author Information

Gilles Louppe (New York University)
Michael Kagan (SLAC / Stanford)
Kyle Cranmer (New York University)

Kyle Cranmer is an Associate Professor of Physics at New York University and affiliated with NYU's Center for Data Science. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July, 2012. His current interests are at the intersection of physics and machine learning and include inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, adversarial training for robustness to systematic uncertainty, the use of generative models in the physical sciences, and integration of reproducible workflows in the inference pipeline.

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