Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector D de Florian, C Grojean, F Maltoni, C Mariotti, A Nikitenko, M Pieri, ... arXiv. org, 2016 | 879 | 2016 |

Pushing Higgs effective theory to its limits J Brehmer, A Freitas, D Lopez-Val, T Plehn Physical Review D 93 (7), 075014, 2016 | 94 | 2016 |

Symmetry Restored in Dibosons at the LHC? J Brehmer, JA Hewett, J Kopp, T Rizzo, J Tattersall Journal of High Energy Physics 2015 (10), 182, 2015 | 87 | 2015 |

Constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical review letters 121 (11), 111801, 2018 | 42 | 2018 |

Extending the limits of Higgs effective theory A Biekötter, J Brehmer, T Plehn Physical Review D 94 (5), 055032, 2016 | 42* | 2016 |

A guide to constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical Review D 98 (5), 052004, 2018 | 38 | 2018 |

Better Higgs boson measurements through information geometry J Brehmer, K Cranmer, F Kling, T Plehn Physical Review D 95 (7), 073002, 2017 | 37 | 2017 |

Mining gold from implicit models to improve likelihood-free inference J Brehmer, G Louppe, J Pavez, K Cranmer arXiv preprint arXiv:1805.12244, 2018 | 23 | 2018 |

Better Higgs- tests through information geometry J Brehmer, F Kling, T Plehn, TMP Tait Physical Review D 97 (9), 095017, 2018 | 20 | 2018 |

Polarized W W scattering on the Higgs pole J Brehmer, J Jaeckel, T Plehn Physical Review D 90 (5), 054023, 2014 | 15 | 2014 |

The diboson excess: experimental situation and classification of explanations; a Les Houches pre-proceeding J Brehmer, G Brooijmans, G Cacciapaglia, A Carmona, SR Chivukula, ... arXiv preprint arXiv:1512.04357, 2015 | 14 | 2015 |

Neural Message Passing for Jet Physics I Henrion, J Brehmer, J Bruna, K Cho, K Cranmer, G Louppe, G Rochette | 12 | 2017 |

Likelihood-free inference with an improved cross-entropy estimator M Stoye, J Brehmer, G Louppe, J Pavez, K Cranmer arXiv preprint arXiv:1808.00973, 2018 | 8 | 2018 |

Effective LHC measurements with matrix elements and machine learning J Brehmer, K Cranmer, I Espejo, F Kling, G Louppe, J Pavez arXiv preprint arXiv:1906.01578, 2019 | 4 | 2019 |

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning J Brehmer, S Mishra-Sharma, J Hermans, G Louppe, K Cranmer arXiv preprint arXiv:1909.02005, 2019 | 3* | 2019 |

New Ideas for Effective Higgs Measurements J Brehmer | 3 | 2017 |

Towards testing a two-Higgs-doublet model with maximal CP symmetry at the LHC: Monte Carlo event generator implementation J Brehmer, V Lendermann, M Maniatis, O Nachtmann, HC Schultz-Coulon, ... The European Physical Journal C 73 (4), 2380, 2013 | 3 | 2013 |

MadMiner: Machine learning-based inference for particle physics J Brehmer, F Kling, I Espejo, K Cranmer arXiv preprint arXiv:1907.10621, 2019 | 2 | 2019 |

Polarised WW Scattering at the LHC J Brehmer | 2 | 2012 |

Monte-Carlo event generation for a two-Higgs-doublet model with maximal CP symmetry J Brehmer arXiv preprint arXiv:1206.7044, 2012 | 1 | 2012 |