Fetching the paper…
Reading the bibliography…
In this work we discuss the impact of nuisance parameters on the effectiveness of machine learning in high-energy physics problems, and provide a review of techniques that allow to include their effect and reduce their impact in the search for optimal selection criteria and variable transformations.
1905
Earlier work this paper cites.
1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
K. Cranmer, J. Brehmer, and G. Louppe, The frontier of simulation-based inference
1911
Earlier work this paper cites.
J. Neyman and E. S. Pearson, On the problem of the most efficient tests of statistical hypotheses
1933
Earlier work this paper cites.
R. Dalitz, On the analysis of tau-meson data and the nature of the tau-meson
1953
Earlier work this paper cites.
F. James and M. Roos, Minuit - a system for function minimization and analysis of the parameter errors and correlations
1975
Earlier work this paper cites.
W. M. Patefield, On the maximized likelihood function
1977
Earlier work this paper cites.
Morgan-Kaufmann, 1992
P. Simard, B. Victorri, Y. LeCun, and J. Denker, Tangent prop - a formalism for specifying selected invariances in an adaptive network · 1992
Earlier work this paper cites.
CRC Press, Mar., 1994
D. R. Cox and O. E. Barndorff-Nielsen, Inference and Asymptotics · 1994
Earlier work this paper cites.
Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting
1997
Earlier work this paper cites.
G. Kasieczka and D. Shih, DisCo Fever: Robust Networks Through Distance Correlation
2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
MIT Press, 2007
S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira, Analysis of representations for domain adaptation · 2007
Earlier work this paper cites.
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, A theory of learning from different domains
2009
Earlier work this paper cites.
J. Thaler and K. Van Tilburg, Maximizing Boosted Top Identification by Minimizing N-subjettiness
2012
Cited alongside, same era.
2012
Cited alongside, same era.
2012
Cited alongside, same era.
Pearson Education, 2012
M. H. DeGroot and M. J. Schervish, Probability and statistics · 2012
Cited alongside, same era.
2013
Cited alongside, same era.
2017
Later among the works it cites.
L. M. Dery, B. Nachman, F. Rubbo, and A. Schwartzman, Weakly supervised classification in high energy physics
2017
Later among the works it cites.
E. M. Metodiev, B. Nachman, and J. Thaler, Classification without labels: learning from mixed samples in high energy physics
2017
Later among the works it cites.
2018
Later among the works it cites.
S. Chang, T. Cohen, and B. Ostdiek, What is the Machine Learning?
2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2013
Cited alongside, same era.
2014
Cited alongside, same era.
2014
Cited alongside, same era.
2015
Cited alongside, same era.
C. Adam-Bourdarios, G. Cowan, C. Germain-Renaud, I. Guyon, B. Kégl, and D. Rousseau, The Higgs Machine Learning Challenge
2015
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Later among the works it cites.
T. Cohen, M. Freytsis, and B. Ostdiek, (machine) learning to do more with less
2018
Later among the works it cites.
P. T. Komiske, E. M. Metodiev, B. Nachman, and M. D. Schwartz, Learning to classify from impure samples with high-dimensional data
2018
Later among the works it cites.
T. Charnock, G. Lavaux, and B. D. Wandelt, Automatic physical inference with information maximizing neural networks
2018
Later among the works it cites.
PhD thesis, University of Padova, 2019
P. De Castro Manzano, Statistical Learning and Inference at Particle Collider Experiments · 2019
Later among the works it cites.
2019
Later among the works it cites.
P. de Castro and T. Dorigo, INFERNO: Inference-Aware neural optimisation
2019
Later among the works it cites.
J. Alsing and B. Wandelt, Nuisance hardened data compression for fast likelihood-free inference
2019
Later among the works it cites.
L. Heinrich and N. Simpson, “pyhf/neos: initial zenodo release.” https://doi.org/10.5281/zenodo.3697981 , Mar., 2020
2020
Closest in time.
World Scientific, 2021
Y. Coadou, Chapter 1.1: Boosted Decision Trees · 2021
Closest in time.