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- H. Sebastian Seung, Manfred Opper, Haim Sompolinsky
- COLT
- 1992

We propose an algorithm called query by commitee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The… (More)

- Lehel Csató, Manfred Opper
- Neural Computation
- 2002

We develop an approach for sparse representations of gaussian process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method… (More)

- Manfred Opper
- 2008

We develop an approach for sparse representations of Gaussian Process (GP) models (which are Bayesian types of kernel machines) in order to overcome their limitations for large data sets. The method… (More)

- Manfred Opper, Ole Winther
- Neural Computation
- 2000

We derive a mean-field algorithm for binary classification with gaussian processes that is based on the TAP approach originally proposed in statistical physics of disordered systems. The theory also… (More)

- Manfred Opper, Cédric Archambeau
- Neural Computation
- 2009

The variational approximation of posterior distributions by multivariate gaussians has been much less popular in the machine learning community compared to the corresponding approximation by… (More)

- Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor
- Gaussian Processes in Practice
- 2007

Some of the most complex models routinely run are numerical weather prediction models. These models are based on a discretisation of a coupled set of partial differential equations (the dynamics)… (More)

Assume fP : 2 g is a set of probability distributions with a common dominating measure on a complete separable metric space Y . A state 2 is chosen by Nature. A statistician gets n independent… (More)

- Lehel Csató, Manfred Opper
- NIPS
- 2000

We develop an approach for a sparse representation for Gauss ian Process (GP) models in order to overcome the limitations of GPs cause d by large data sets. The method is based on a combination of a… (More)

- Hilbert J. Kappen, Vicenç Gómez, Manfred Opper
- Machine Learning
- 2012

We reformulate a class of non-linear stochastic optimal control problems introduced by Todorov (in Advances in Neural Information Processing Systems, vol. 19, pp. 1369–1376, 2007) as a… (More)

- Manfred Opper, Ole Winther
- Journal of Machine Learning Research
- 2005

We propose a novel framework for deriving approximations for intractable probabilistic models. This framework is based on a free energy (negative log marginal likelihood) and can be seen as a… (More)