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Causal Inference on Discrete Data using Additive Noise Models
Jonas Peters, Dominik Janzing, Bernhard Schölkopf
Inferring the causal structure of a set of random variables from a finite sample of the joint distribution is an important problem in science. Recently, methods using additive nois…
Telling cause from effect based on high-dimensional observations
Dominik Janzing, Patrik O. Hoyer, Bernhard Schoelkopf
We describe a method for inferring linear causal relations among multi-dimensional variables. The idea is to use an asymmetry between the distributions of cause and effect that occ…
Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing
Sarah Bridle, Sreekumar T. Balan, Matthias Bethge +31
We present the results of the GREAT08 Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. The primary goal was to stimulate new…
Hilbert space embeddings and metrics on probability measures
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu +2
A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing, and independence testing.…
On integral probability metrics, ϕ-divergences and binary classification
Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton +2
A class of distance measures on probabilities -- the integral probability metrics (IPMs) -- is addressed: these include the Wasserstein distance, Dudley metric, and Maximum Mean Di…