53 citations · 158 across the 12 of their papers we have counts for
12 papers
Deep Hypothesis Tests Detect Clinically Relevant Subgroup Shifts in Medical Images
Lisa M. Koch, Christian M. Schürch, Christian F. Baumgartner +2
Distribution shifts remain a fundamental problem for the safe application of machine learning systems. If undetected, they may impact the real-world performance of such systems or…
AI for Science: An Emerging Agenda
Philipp Berens, Kyle Cranmer, Neil D. Lawrence +2
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's scientific ch…
Efficient identification of informative features in simulation-based inference
Jonas Beck, Michael Deistler, Yves Bernaerts +2
Simulation-based Bayesian inference (SBI) can be used to estimate the parameters of complex mechanistic models given observed model outputs without requiring access to explicit lik…
Unsupervised visualization of image datasets using contrastive learning
Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
Visualization methods based on the nearest neighbor graph, such as t-SNE or UMAP, are widely used for visualizing high-dimensional data. Yet, these approaches only produce meaningf…
Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering
Sacha Sokoloski, Philipp Berens
We introduce hierarchical mixtures of Gaussians (HMoGs), which unify dimensionality reduction and clustering into a single probabilistic model. HMoGs provide closed-form expression…
Sparse Visual Counterfactual Explanations in Image Space
Valentyn Boreiko, Maximilian Augustin, Francesco Croce +2
Visual counterfactual explanations (VCEs) in image space are an important tool to understand decisions of image classifiers as they show under which changes of the image the decisi…