Mining gold from implicit models to improve likelihood-free inference. - Johann Brehmer; Gilles Louppe; Juan Pavez; Kyle Cranmer (CoRR, 2018)

Likelihood-free inference with an improved cross-entropy estimator. - Markus Stoye; Johann Brehmer; Gilles Louppe; Juan Pavez; Kyle Cranmer (CoRR, 2018)

The frontier of simulation-based inference. - Kyle Cranmer; Johann Brehmer; Gilles Louppe (CoRR, 2019)

Flows for simultaneous manifold learning and density estimation. - Johann Brehmer; Kyle Cranmer (NeurIPS, 2020)

Hierarchical clustering in particle physics through reinforcement learning. - Johann Brehmer; Sebastian Macaluso; Duccio Pappadopulo; Kyle Cranmer (CoRR, 2020)

MadMiner: Machine Learning-Based Inference for Particle Physics. - Johann Brehmer; Felix Kling; Irina Espejo; Kyle Cranmer (Comput. Softw. Big Sci., 2020)

Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. - Ties van Rozendaal; Johann Brehmer; Yunfan Zhang; Reza Pourreza 002; Taco S. Cohen (CoRR, 2021)

Implicit Neural Video Compression. - Yunfan Zhang; Ties van Rozendaal; Johann Brehmer; Markus Nagel; Taco Cohen (CoRR, 2021)

Weakly supervised causal representation learning. - Johann Brehmer; Pim de Haan; Phillip Lippe; Taco S. Cohen (NeurIPS, 2022)

Deconfounded Imitation Learning. - Risto Vuorio; Johann Brehmer; Hanno Ackermann; Daniel Dijkman; Taco Cohen; Pim de Haan (CoRR, 2022)

EDGI: Equivariant Diffusion for Planning with Embodied Agents. - Johann Brehmer; Joey Bose; Pim de Haan; Taco S. Cohen (NeurIPS, 2023)

Geometric Algebra Transformer. - Johann Brehmer; Pim de Haan; Sönke Behrends; Taco S. Cohen (NeurIPS, 2023)

Geometric Algebra Transformers. - Johann Brehmer; Pim de Haan; Sönke Behrends; Taco Cohen (CoRR, 2023)

FoMo Rewards: Can we cast foundation models as reward functions? - Ekdeep Singh Lubana; Johann Brehmer; Pim de Haan; Taco Cohen (CoRR, 2023)

Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. - Ties van Rozendaal; Johann Brehmer; Yunfan Zhang; Reza Pourreza 002; Auke J. Wiggers; Taco Cohen (Trans. Mach. Learn. Res., 2023)

Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. - Pim de Haan; Taco Cohen; Johann Brehmer (AISTATS, 2024)

Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics. - Jonas Spinner; Victor Bresó; Pim de Haan; Tilman Plehn; Jesse Thaler; Johann Brehmer (NeurIPS, 2024)

Probabilistic and Differentiable Wireless Simulation with Geometric Transformers. - Thomas Hehn 001; Markus Peschl; Tribhuvanesh Orekondy; Arash Behboodi; Johann Brehmer (CoRR, 2024)

A Lorentz-Equivariant Transformer for All of the LHC. - Johann Brehmer; Victor Bresó; Pim de Haan; Tilman Plehn; Huilin Qu; Jonas Spinner; Jesse Thaler (CoRR, 2024)

Deconfounding Imitation Learning with Variational Inference. - Risto Vuorio; Pim de Haan; Johann Brehmer; Hanno Ackermann; Daniel Dijkman; Taco Cohen (Trans. Mach. Learn. Res., 2024)

Differentiable and Learnable Wireless Simulation with Geometric Transformers. - Thomas Hehn 001; Markus Peschl; Tribhuvanesh Orekondy; Arash Behboodi; Johann Brehmer (ICLR, 2025)

Does equivariance matter at scale? - Johann Brehmer; Sönke Behrends; Pim de Haan; Taco Cohen (Trans. Mach. Learn. Res., 2025)