6.4k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- ETH ZurichCH70 papers
- Centre de Recherche en Économie et StatistiqueFR4 papers
- Max Planck Institute for Intelligent SystemsDE4 papers
- University of ZurichCH4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- University of BernCH3 papers
- Carnegie Mellon UniversityUS2 papers
- Delft University of TechnologyNL2 papers
- Heidelberg UniversityDE2 papers
- Institute for Biomedical EngineeringCH2 papers
- Laboratoire d’Analyse et de Mathématiques AppliquéesFR2 papers
- Ludwig-Maximilians-Universität MünchenDE2 papers
10 papers · 1 filter
Order-independent constraint-based causal structure learning
Diego Colombo, Marloes H. Maathuis
We consider constraint-based methods for causal structure learning, such as the PC-, FCI-, RFCI- and CCD- algorithms (Spirtes et al. (2000, 1993), Richardson (1996), Colombo et al.…
Correlated variables in regression: clustering and sparse estimation
Peter Bühlmann, Philipp Rütimann, Sara van de Geer +1
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as…
Hypersurfaces and their singularities in partial correlation testing
Shaowei Lin, Caroline Uhler, Bernd Sturmfels +1
An asymptotic theory is developed for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations.…
A Simulated Annealing Approach to Approximate Bayes Computations
Carlo Albert, Hans R. Kuensch, Andreas Scheidegger
Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function of the model is expensive to evaluate but relatively cheap to sample from. In par…
Bridging the ensemble Kalman and particle filter
Marco Frei, Hans R. Künsch
In many applications of Monte Carlo nonlinear filtering, the propagation step is computationally expensive, and hence, the sample size is limited. With small sample sizes, the upda…
Causal Inference on Time Series using Structural Equation Models
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Causal inference uses observations to infer the causal structure of the data generating system. We study a class of functional models that we call Time Series Models with Independe…
A robust, adaptive M-estimator for pointwise estimation in heteroscedastic regression
Michaël Chichignoud, Johannes Lederer
We introduce a robust and fully adaptive method for pointwise estimation in heteroscedastic regression. We allow for noise and design distributions that are unknown and fulfill ver…
Two Optimal Strategies for Active Learning of Causal Models from Interventional Data
Alain Hauser, Peter Bühlmann
From observational data alone, a causal DAG is only identifiable up to Markov equivalence. Interventional data generally improves identifiability; however, the gain of an intervent…
Identifiability of Gaussian structural equation models with equal error variances
Jonas Peters, Peter Bühlmann
We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding…
Statistical significance in high-dimensional linear models
Peter Bühlmann
We propose a method for constructing p-values for general hypotheses in a high-dimensional linear model. The hypotheses can be local for testing a single regression parameter or th…