Johann Brehmer

Papers

For a more or less complete list, see Google Scholar, ADS, INSPIRE, or Papers with Code. Here I want to highlight a few favorites:

Probabilistic / generative models

• Johann Brehmer and Kyle Cranmer:
  Flows for simultaneous manifold learning and density estimation.
  NeurIPS 2020. [arXiv] [conference] [bibtex] [code]
  (Also: Spotlight talk at the ICML workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (2020). [workshop])

Simulation-based inference methods

• Kyle Cranmer, Johann Brehmer, and Gilles Louppe:
  The frontier of simulation-based inference.
  Proceedings of the National Academy of Science 117 (2020). [arXiv] [journal] [bibtex]
• Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer:
  Mining gold from implicit models to improve likelihood-free inference.
  Proceedings of the National Academy of Science 117 (2020). [arXiv] [journal] [bibtex]
  (Also: NeurIPS workshop on Machine Learning and the Physical Sciences (2019). [workshop])
• Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer:
  Likelihood-free inference with an improved cross-entropy estimator.
  NeurIPS workshop on Machine Learning and the Physical Sciences (2019). [arXiv] [workshop] [bibtex]

Simulation-based inference in physics

• Johann Brehmer and Kyle Cranmer:
  Simulation-based inference methods for particle physics.
  To appear in Artificial Intelligence for Particle Physics. [arXiv] [bibtex]
• Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, and Kyle Cranmer:
  Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning.
  The Astrophysical Journal 886 (2019). [arXiv] [journal] [bibtex] [code]
• Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer:
  MadMiner: Machine learning-based inference for particle physics.
  Computing and Software for Big Science 4 (2020). [arXiv] [journal] [bibtex] [code]
• Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez:
  Constraining effective field theories with machine learning.
  Physical Review Letters 121 (2018). [arXiv] [journal] [bibtex] [code]
• Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez:
  A guide to constraining effective field theories with machine learning.
  Physical Review D 98 (2018). [arXiv] [journal] [bibtex] [code]

More deep learning for physics

• Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, and Kyle Cranmer:
  Hierarchical clustering in particle physics through reinforcement learning.
  NeurIPS workshop on Machine Learning and the Physical Sciences (2020). [arXiv] [bibtex] [code]
• Isaac Henrion, Johann Brehmer, Joan Bruna, Kyunghun Cho, Kyle Cranmer, Gilles Louppe, and Gaspar Rochette:
  Neural Message Passing for Jet Physics.
  NeurIPS workshop on Deep Learning for the Physical Sciences (2017). [workshop]

Information geometry, experimental design, and particle physics walk into a bar

• Johann Brehmer, Kyle Cranmer, Felix Kling, Tilman Plehn:
  Better Higgs Measurements Through Information Geometry.
  Physical Review D 95 (2017). [arXiv] [journal] [bibtex]

Particle physics theory

• Johann Brehmer, Ayres Freitas, David Lopez-Val, Tilman Plehn:
  Pushing Higgs Effective Theory to its Limits.
  Physical Review D 93 (2016). [arXiv] [journal] [bibtex]