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
8 papers · 1 filter
Hierarchical Testing in the High-Dimensional Setting with Correlated Variables
Jacopo Mandozzi, Peter Bühlmann
We propose a method for testing whether hierarchically ordered groups of potentially correlated variables are significant for explaining a response in a high-dimensional linear mod…
High-dimensional learning of linear causal networks via inverse covariance estimation
Po-Ling Loh, Peter Bühlmann
We establish a new framework for statistical estimation of directed acyclic graphs (DAGs) when data are generated from a linear, possibly non-Gaussian structural equation model. Ou…
Score-based Causal Learning in Additive Noise Models
Christopher Nowzohour, Peter Bühlmann
Given data sampled from a number of variables, one is often interested in the underlying causal relationships in the form of a directed acyclic graph. In the general case, without…
On the uniform convergence of empirical norms and inner products, with application to causal inference
Sara van de Geer
Uniform convergence of empirical norms - empirical measures of squared functions - is a topic which has received considerable attention in the literature on empirical processes. Th…
Particle filters
Hans R. Künsch
This is a short review of Monte Carlo methods for approximating filter distributions in state space models. The basic algorithm and different strategies to reduce imbalance of the…
Causal Discovery with Continuous Additive Noise Models
Jonas Peters, Joris Mooij, Dominik Janzing +1
We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional exper…
Group-bound: confidence intervals for groups of variables in sparse high-dimensional regression without assumptions on the design
Nicolai Meinshausen
It is in general challenging to provide confidence intervals for individual variables in high-dimensional regression without making strict or unverifiable assumptions on the design…
Random Intersection Trees
Rajen Dinesh Shah, Nicolai Meinshausen
Finding interactions between variables in large and high-dimensional datasets is often a serious computational challenge. Most approaches build up interaction sets incrementally, a…