Machine Learning on data with sPlot background subtraction

M. Pivk and F. R. Le Diberder, splot: A statistical tool to unfold data distributions , Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 555

2005

Earlier work this paper cites.

A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne, H. Voss et al., TMVA – Toolkit for multivariate data analysis , arXiv preprint physics/0703039 (2007)

2007

Earlier work this paper cites.

D. Martschei, M. Feindt, S. Honc and J. Wagner-Kuhr, Advanced event reweighting using multivariate analysis , in Journal of Physics: Conference Series , vol. 368, p. 012028, IOP Publishing, 2012

2012

Earlier work this paper cites.

P. Baldi, P. Sadowski and D. Whiteson, Searching for exotic particles in high-energy physics with deep learning , Nature communications 5

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization , arXiv preprint arXiv:1412.6980 (2014)

Original

2014

Cited alongside, same era.

B. Lipp, sPlot-based training of multivariate classifiers in the Belle II analysis software framework , Bachelor thesis, KIT (2015)

2015

Cited alongside, same era.

PhD thesis, KIT, Karlsruhe, 2017

T. Keck, Machine learning algorithms for the Belle II experiment and their validation on Belle data · 2017

Cited alongside, same era.

E. M. Metodiev, B. Nachman and J. Thaler, Classification without labels: Learning from mixed samples in high energy physics , Journal of High Energy Physics 2017

2017

Cited alongside, same era.

D. Dua and C. Graff, UCI machine learning repository , 2017

2017

Later among the works it cites.

L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush and A. Gulin, CatBoost: unbiased boosting with categorical features , in Advances in Neural Information Processing Systems , pp. 6638–6648, 2018

2018

Later among the works it cites.

“GitHub issue: Ensemble models (and maybe others?) don’t check for negative sample_weight, last accesed: 2019-06-01.” https://github.com/scikit-learn/scikit-learn/issues/3774

2019

Closest in time.