Systematic aware learning

作者：V. Estrade, Cécile Germain, Isabelle Guyon, David Rousseau · 发表于：EPJ Web of Conferences · 年份：2019 · DOI：10.1051/epjconf/201921406024 · 被引用次数：6 · 研究领域：Computer Science

Experimental science often has to cope with systematic errors that coherently bias data. We analyze this issue on the analysis of data produced by experiments of the Large Hadron Collider at CERN as a case of supervised domain adaptation. Systematics-aware learning should create an efficient representation that is insensitive to perturbations induced by the systematic effects. We present an experimental comparison of the adversarial knowledge-free approach and a less data-intensive alternative.