Person

Matthias Seeger

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Related publications (56)

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Gaussian Process Belief Propagation

Matthias Seeger

The framework of graphical models is a cornerstone of applied Statistics, allowing for an intuitive graphical specification of the main features of a model, and providing a basis for general Bayesian inference computations though belief propagation (BP). I ...

MIT Press2007

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

Matthias Seeger

We propose a highly efficient framework for kernel multi-class models with a large and structured set of classes. Kernel parameters are learned automatically by maximizing the cross-validation log likelihood, and predictive probabilities are estimated. We ...

2007

A Note on Log-Concavity

Matthias Seeger

This is a small observation concerning scale mixtures and their log-concavity. ...

2007

Bayesian Modelling in Machine Learning: A Tutorial Review

Matthias Seeger

Many facets of Bayesian Modelling are firmly established in Machine Learning and give rise to state-of-the-art solutions to application problems. The sheer number of techniques, ideas and models which have been proposed, and the terminology, can be bewilde ...

2006

Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations

Matthias Seeger

Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...

2006

Worst-Case Bounds for Gaussian Process Models

Matthias Seeger

We present a competitive analysis of some non-parametric Bayesian algorithms in a worst-case online learning setting, where no probabilistic assumptions about the generation of the data are made. We consider models which use a Gaussian process prior (over ...

2006

Fast Gaussian Process Regression Using KD-Trees

Matthias Seeger

The computation required for Gaussian process regression with n training examples is about O(n^3) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximati ...

2006

A Taxonomy for Semi-Supervised Learning Methods

Matthias Seeger

We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning problem. We give some broad classes of algorithms for each of the families and point to specific realizations in the literature. Finally, we shed more detailed ...

MIT Press2006

Multivariate Nonlinear Regression with Semiparametric Latent Factor Models

Matthias Seeger

We propose a semiparametric model for regression problems involving multiple response variables. Conditional dependencies between the responses are represented through a linear mixture of Gaussian processes. We propose an efficient approximate inference sc ...

2005

Semiparametric Latent Factor Models

Matthias Seeger

We propose a semiparametric model for regression problems involving multiple response variables. The model makes use of a set of Gaussian processes that are linearly mixed to capture dependencies that may exist among the response variables. We propose an e ...

2005