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Confidence and Uncertainty Assessment for Distributional Random Forests
Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann +1
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions. Due to its general estimation procedure,…
High Probability Lower Bounds for the Total Variation Distance
Loris Michel, Jeffrey Näf, Nicolai Meinshausen
The statistics and machine learning communities have recently seen a growing interest in classification-based approaches to two-sample testing. The outcome of a classification-base…
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…
Random Projections For Large-Scale Regression
Gian-Andrea Thanei, Christina Heinze, Nicolai Meinshausen
Fitting linear regression models can be computationally very expensive in large-scale data analysis tasks if the sample size and the number of variables are very large. Random proj…
On b-bit min-wise hashing for large-scale regression and classification with sparse data
Rajen D. Shah, Nicolai Meinshausen
Large-scale regression problems where both the number of variables, , and the number of observations, , may be large and in the order of millions or more, are becoming increa…
Discussion: Latent variable graphical model selection via convex optimization
Steffen Lauritzen, Nicolai Meinshausen
Discussion of "Latent variable graphical model selection via convex optimization" by Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky [arXiv:1008.1290].