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
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach
Niklas Pfister, Stefan Bauer, Jonas Peters
Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference…
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…
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…
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…
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…
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.…
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…
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…
Separating populations with wide data: A spectral analysis
Avrim Blum, Amin Coja-Oghlan, Alan Frieze +1
In this paper, we consider the problem of partitioning a small data sample drawn from a mixture of product distributions. We are interested in the case that individual features…