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Conditional Variance Penalties and Domain Shift Robustness
Christina Heinze-Deml, Nicolai Meinshausen
When training a deep neural network for image classification, one can broadly distinguish between two types of latent features of images that will drive the classification. We can…
Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence
Luca Weihs, Mathias Drton, Nicolai Meinshausen
The need to test whether two random vectors are independent has spawned a large number of competing measures of dependence. We are interested in nonparametric measures that are inv…
Causal Structure Learning
Christina Heinze-Deml, Marloes H. Maathuis, Nicolai Meinshausen
Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph. Causal models can be viewed as a special class of graphical models that n…
Invariant Causal Prediction for Nonlinear Models
Christina Heinze-Deml, Jonas Peters, Nicolai Meinshausen
An important problem in many domains is to predict how a system will respond to interventions. This task is inherently linked to estimating the system's underlying causal structure…
Causal Dantzig: fast inference in linear structural equation models with hidden variables under additive interventions
Dominik Rothenhäusler, Peter Bühlmann, Nicolai Meinshausen
Causal inference is known to be very challenging when only observational data are available. Randomized experiments are often costly and impractical and in instrumental variable re…
Preserving Differential Privacy Between Features in Distributed Estimation
Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
Privacy is crucial in many applications of machine learning. Legal, ethical and societal issues restrict the sharing of sensitive data making it difficult to learn from datasets th…