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]