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 | 1223 | 2016 |

Pushing Higgs effective theory to its limits J Brehmer, A Freitas, D Lopez-Val, T Plehn Physical Review D 93 (7), 075014, 2016 | 99 | 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 | 89 | 2015 |

Constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical Review Letters 121 (11), 111801, 2018 | 75 | 2018 |

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 | 64 | 2018 |

Extending the limits of Higgs effective theory A Biekötter, J Brehmer, T Plehn Physical Review D 94 (5), 055032, 2016 | 46* | 2016 |

Mining gold from implicit models to improve likelihood-free inference J Brehmer, G Louppe, J Pavez, K Cranmer Proceedings of the National Academy of Sciences, 2020, 2018 | 45 | 2018 |

Better Higgs boson measurements through information geometry J Brehmer, K Cranmer, F Kling, T Plehn Physical Review D 95 (7), 073002, 2017 | 41 | 2017 |

Better Higgs- tests through information geometry J Brehmer, F Kling, T Plehn, TMP Tait Physical Review D 97 (9), 095017, 2018 | 30 | 2018 |

Neural Message Passing for Jet Physics I Henrion, J Brehmer, J Bruna, K Cho, K Cranmer, G Louppe, G Rochette NIPS Workshop on Deep Learning for the Physical Sciences 2017, 2017 | 25 | 2017 |

The frontier of simulation-based inference K Cranmer, J Brehmer, G Louppe Proceedings of the National Academy of Sciences, 2020 | 24 | 2020 |

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 The Astrophysical Journal 886 (1), 49, 2019 | 15 | 2019 |

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

Likelihood-free inference with an improved cross-entropy estimator M Stoye, J Brehmer, G Louppe, J Pavez, K Cranmer NeurIPS Workshop on Machine Learning for the Physical Sciences 2019, 2018 | 14 | 2018 |

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 |

MadMiner: Machine learning-based inference for particle physics J Brehmer, F Kling, I Espejo, K Cranmer Computing and Software for Big Science 4 (1), 1-25, 2020 | 12 | 2020 |

Benchmarking simplified template cross sections in *W H* productionJ Brehmer, S Dawson, S Homiller, F Kling, T Plehn Journal of High Energy Physics 2019 (11), 34, 2019 | 12 | 2019 |

Effective LHC measurements with matrix elements and machine learning J Brehmer, K Cranmer, I Espejo, F Kling, G Louppe, J Pavez Journal of Physics: Conference Series 1525 (1), 012022, 2020 | 9 | 2020 |

New ideas for effective higgs measurements J Brehmer | 4 | 2017 |

The diboson excess: experimental situation and classification of explanations J Brehmer, G Brooijmans, G Cacciapaglia A Les Houches pre-proceeding, 2015 | 4 | 2015 |