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Machine learning tools are commonly used in modern high energy physics (HEP) experiments.
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Friedman, J. (2003). On Multivariate Goodness-of-Fit and Two-Sample Testing. In Statistical Problems in Particle Physics, Astrophysics, and Cosmology (Vol. 1, p. 311)
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ATLAS collaboration. (2012). Summary of ATLAS Pythia 8 tunes (Vol. 14). ATL-PHYS-PUB-2012-003
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Sugiyama, M., Suzuki, T., Kanamori, T. Density Ratio Estimation in Machine Learning, Cambridge University Press, Cambridge, UK, 2012
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Kizilcec, R. Reducing non-response bias with survey reweighting: Applications for online learning researchers. Proceedings of the first ACM conference on Learning @ scale conference. ACM, 2014
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https://github.com/arogozhnikov/hep_ml
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems (pp. 2672-2680)
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Likhomanenko, T., Ilten, P., Khairullin, E., Rogozhnikov, A., Ustyuzhanin, A., Williams, M. (2015). LHCb Topological Trigger Reoptimization. In Journal of Physics: Conference Series (Vol. 664, No. 8, p. 082025). IOP Publishing
2015
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Blake, T., Bettler, M.-O., Chrzaszcz, M., Dettori, F., Ustyuzhanin, A., and Likhomanenko, T. (2015). Flavours of physics: the machine learning challenge or the search of τ → 3 μ \tau\to 3\mu decays at LHCb
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