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Bayesian Active Learning for Classification and Preference Learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani +1
Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks…
Gaussian Process Regression Networks
Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric f…
Pitman-Yor Diffusion Trees
David A. Knowles, Zoubin Ghahramani
We introduce the Pitman Yor Diffusion Tree (PYDT) for hierarchical clustering, a generalization of the Dirichlet Diffusion Tree (Neal, 2001) which removes the restriction to binary…
Bayesian and L1 Approaches to Sparse Unsupervised Learning
Shakir Mohamed, Katherine Heller, Zoubin Ghahramani
The use of L1 regularisation for sparse learning has generated immense research interest, with successful application in such diverse areas as signal acquisition, image coding, gen…