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Gaussian Processes for Machine Learning
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
TLDR
The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and includes detailed algorithms for supervised-learning problem for both regression and classification.
A Unifying View of Sparse Approximate Gaussian Process Regression
TLDR
A new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression, relies on expressing the effective prior which the methods are using, and highlights the relationship between existing methods.
The Infinite Gaussian Mixture Model
  • C. Rasmussen
  • Mathematics, Computer Science
    NIPS
  • 29 November 1999
TLDR
This paper presents an infinite Gaussian mixture model which neatly sidesteps the difficult problem of finding the "right" number of mixture components and uses an efficient parameter-free Markov Chain that relies entirely on Gibbs sampling.
PILCO: A Model-Based and Data-Efficient Approach to Policy Search
TLDR
PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way by learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning.
Gaussian Processes for Regression
TLDR
This paper investigates the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis for fixed values of hyperparameters to be carried out exactly using matrix operations.
The Infinite Hidden Markov Model
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the
Gaussian Processes for Data-Efficient Learning in Robotics and Control
TLDR
This paper learns a probabilistic, non-parametric Gaussian process transition model of the system and applies it to autonomous learning in real robot and control tasks, achieving an unprecedented speed of learning.
Sparse Spectrum Gaussian Process Regression
TLDR
The achievable trade-offs between predictive accuracy and computational requirements are compared, and it is shown that these are typically superior to existing state-of-the-art sparse approximations.
Evaluation of gaussian processes and other methods for non-linear regression
TLDR
It is shown that a Bayesian approach to learning in multi-layer perceptron neural networks achieves better performance than the commonly used early stopping procedure, even for reasonably short amounts of computation time.
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