489 citations · 949 across the 29 of their papers we have counts for
3 papers · 1 filter
A Cautionary Tale of Decorrelating Theory Uncertainties
Aishik Ghosh, Benjamin Nachman
A variety of techniques have been proposed to train machine learning classifiers that are independent of a given feature. While this can be an essential technique for enabling back…
Presenting Unbinned Differential Cross Section Results
Miguel Arratia, Anja Butter, Mario Campanelli +10
Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many dimensions. Unbinne…
Uncertainty Aware Learning for High Energy Physics
Aishik Ghosh, Benjamin Nachman, Daniel Whiteson
Machine learning techniques are becoming an integral component of data analysis in High Energy Physics (HEP). These tools provide a significant improvement in sensitivity over trad…