1.1k citations · 2.1k across the 27 of their papers we have counts for
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Weakly supervised causal representation learning
Johann Brehmer, Pim de Haan, Phillip Lippe +1
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under…
Flows for simultaneous manifold learning and density estimation
Johann Brehmer, Kyle Cranmer
We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold as well as a tractable probability density on that mani…
The frontier of simulation-based inference
Kyle Cranmer, Johann Brehmer, Gilles Louppe
Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for infer…
Likelihood-free inference with an improved cross-entropy estimator
Markus Stoye, Johann Brehmer, Gilles Louppe +2
We extend recent work (Brehmer, et. al., 2018) that use neural networks as surrogate models for likelihood-free inference. As in the previous work, we exploit the fact that the joi…
Mining gold from implicit models to improve likelihood-free inference
Johann Brehmer, Gilles Louppe, Juan Pavez +1
Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often i…