Inference of latent variables from complicated data is one important problem in data mining. The high dimensionality and high complexity of real world data often make accurate infe...
This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modell...
The estimation of linear causal models (also known as structural equation models) from data is a well-known problem which has received much attention in the past. Most previous wo...
Patrik O. Hoyer, Shohei Shimizu, Antti J. Kerminen
We present an algorithm to infer causal relations between a set of measured variables on the basis of experiments on these variables. The algorithm assumes that the causal relatio...
Frederick Eberhardt, Patrik O. Hoyer, Richard Sche...
We address an approximation method for Gaussian process (GP) regression, where we approximate covariance by a block matrix such that diagonal blocks are calculated exactly while o...