Christina Heinze

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We propose LOCO, a distributed algorithm which solves large-scale ridge regression. LOCO randomly assigns variables to different processing units which do not communicate. Important dependencies between variables are preserved using random projections which are cheap to compute. We show that LOCO has bounded approximation error compared to the exact ridge(More)
We present Dual-Loco, a communication-ecient algorithm for distributed statistical estimation. Dual-Loco assumes that the data is distributed across workers according to the features rather than the samples. It requires only a single round of communication where low-dimensional random projections are used to approximate the dependencies between features(More)
We propose a simple method to learn linear causal cyclic models in the presence of latent variables. The method relies on equilibrium data of the model recorded under a specific kind of interventions (" shift interventions "). The location and strength of these interventions do not have to be known and can be estimated from the data. Our method, called(More)
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