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:

Geometry

EDGI: Equivariant Diffusion for Planning with Embodied Agents.
Johann Brehmer, Joey Bose, Pim de Haan, and Taco Cohen.
ICLR workshop on reincarnating reinforcement learning (2023). [arXiv]

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

Causality

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

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]

Simulation-based inference

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 deep learning and statistics for 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, Tilman Plehn.
Physical Review D 95 (2017). [arXiv] [journal] [bibtex]

Other flavours of deep learning

Instance-adaptive video compression: Improving neural codecs by training on the test set.
Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, and Taco Cohen.
Under review (2022). [arXiv] [bibtex]

Particle physics theory

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