Bayesian Research Kitchen Workshop (BARK), Grasmere 2008

Bayesian Research Kitchen Workshop (BARK), Grasmere 2008

12 Lectures · Sep 6, 2008

About

Motivation\ The main aim of this workshop is to allow leading Bayesian researchers in machine learning to get together presenting their latest ideas and discussing future directions.

Themes\ * Incorporating Complex Prior Knowledge in Bayesian inference, for example mechanistic models (such as differential equations) or knowledge transfered from other related situations (e.g. hierarchical Dirichlet Processes). * Model mismatch: the Bayesian lynch pin is that the model is correct, but it rarely is. * Approximation techniques: how should we do Bayesian inference in practice. Sampling, variational, Laplace or something else? * Your pet Bayesian issue here.

Visit the Workshop website here.

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01:00:37

Should all Machine Learning be Bayesian? Should all Bayesian models be non-param...

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Introduction to BARK 2008

Neil D. Lawrence

Oct 09, 2008

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On the relation between Bayesian inference and certain solvable problems of stoc...

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Multi-task Learning with Gaussian Processes

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Latent Force Models with Gaussian Processes

Neil D. Lawrence

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Bayesian learning of sparse factor loadings

Magnus Rattray

Oct 09, 2008

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Covariance functions and Bayes errors for GP regression on random graphs

Peter Sollich

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The role of mechanistic models in Bayesian inference

Dan Cornford

Oct 09, 2008

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Probabilistic models for ranking and information extraction

Ed Snelson

Oct 09, 2008

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Well-known shortcomings, advantages and computational challenges in Bayesian mod...

Ole Winther

Oct 09, 2008

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54:54

Variational Model Selection for Sparse Gaussian Process Regression

Michalis K. Titsias

Oct 09, 2008

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Negotiated Interaction : Iterative Inference and Feedback of Intention in HCI

Roderick Murray-Smith

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