Johann Brehmer

Selected papers

You can find a more or less complete list of my papers on Google Scholar. Here I want to highlight a few favorites:

ML + geometry

Geometric algebra transformers.
Johann Brehmer, Pim de Haan, Sönke Behrends, and Taco Cohen.
NeurIPS 2023. [arXiv] [bibtex]

EDGI: Equivariant diffusion for planning with embodied agents.
Johann Brehmer, Joey Bose, Pim de Haan, and Taco Cohen.
NeurIPS 2023. [arXiv] [bibtex]

Flows for simultaneous manifold learning and density estimation.
Johann Brehmer and Kyle Cranmer.
NeurIPS 2020. [arXiv] [conference] [bibtex] [code]

ML + causality

Weakly supervised causal representation learning.
Johann Brehmer, Pim de Haan, Phillip Lippe, and Taco Cohen.
NeurIPS 2022. [arXiv] [bibtex] [code]

Deconfounded imitation learning.
Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, and Pim de Haan.
NeurIPS workshop on deep reinforcement learning (2022). [arXiv]

ML + simulators

The frontier of simulation-based inference.
Kyle Cranmer, Johann Brehmer, and Gilles Louppe.
Proceedings of the National Academy of Science 117 (2020). [arXiv] [journal] [bibtex]

Simulation-based inference methods for particle physics.
Johann Brehmer and Kyle Cranmer.
Book chapter in Artificial Intelligence for High-Energy Physics (World Scientific, 2022). [arXiv] [book] [bibtex]

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning.
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, and Kyle Cranmer.
The Astrophysical Journal 886 (2019). [arXiv] [journal] [bibtex] [code]

MadMiner: Machine learning-based inference for particle physics.
Johann Brehmer, Felix Kling, Irina Espejo, and Kyle Cranmer.
Computing and Software for Big Science 4 (2020). [arXiv] [journal] [bibtex] [code]

Likelihood-free inference with an improved cross-entropy estimator.
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer.
NeurIPS workshop on Machine Learning and the Physical Sciences (2019). [arXiv] [workshop] [bibtex]

Mining gold from implicit models to improve likelihood-free inference.
Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer.
Proceedings of the National Academy of Science 117 (2020). [arXiv] [journal] [bibtex]

Constraining effective field theories with machine learning.
Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez.
Physical Review Letters 121 (2018). [arXiv] [journal] [bibtex] [code]

A guide to constraining effective field theories with machine learning.
Johann Brehmer, Kyle Cranmer, Gilles Louppe, and Juan Pavez.
Physical Review D 98 (2018). [arXiv] [journal] [bibtex] [code]

More ML + physics

Hierarchical clustering in particle physics through reinforcement learning.
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo, and Kyle Cranmer.
NeurIPS workshop on Machine Learning and the Physical Sciences (2020). [arXiv] [workshop] [bibtex] [code]

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

More ML

Instance-adaptive video compression: Improving neural codecs by training on the test set.
Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Auke Wiggers, and Taco Cohen.
Transactions on Machine Learning Research 06/2023. [arXiv] [bibtex]

More physics

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