Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Variational Methods for Graphical Models
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Sparse on-line Gaussian processes
Neural Computation
A family of algorithms for approximate bayesian inference
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Sparse bayesian learning and the relevance vector machine
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Assessing Approximate Inference for Binary Gaussian Process Classification
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Combining feature spaces for classification
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Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics
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Semi-parametric analysis of multi-rater data
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ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation
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Bayesian Generalized Kernel Mixed Models
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A sequential dynamic multi-class model and recursive filtering by variational bayesian methods
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Fast variational inference for gaussian process models through KL-Correction
ECML'06 Proceedings of the 17th European conference on Machine Learning
A case study on meta-generalising: a Gaussian processes approach
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Variational multinomial logit gaussian process
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Nested expectation propagation for Gaussian process classification
The Journal of Machine Learning Research
Stochastic variational inference
The Journal of Machine Learning Research
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It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting. The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.