Nov 3, 2016 · In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal ...

In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property (or, ...

In this work, we introduce a flexible training procedure based on adversarial networks for enforcing the pivotal property on a predictive model.

In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property (or, ...

This work introduces and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property (or, ...

Learning to Pivot with Adversarial Networks. arXiv:1611.01046. Gilles Louppe, Michael Kagan, Kyle Cranmer. Page 2. Testing for new physics. Credits: Jorge Cham.

The paper proposes an adversarial framework for training a classifier that is robust to systematic uncertainties. The paper studies a scenario where the ...

Nov 3, 2016 · In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal ...

Jun 1, 2017 · Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and ...

Learning to Pivot with Adversarial Networks. arXiv:1611.01046. Gilles Louppe, Michael Kagan, Kyle Cranmer. December 15, 2016. Page 2. Systematic ...