Carl E. Rasmussen

According to our database1, Carl E. Rasmussen authored at least 65 papers between 1993 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

Rates of Convergence for Sparse Variational Gaussian Process Regression.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos.
Proceedings of the 35th International Conference on Machine Learning, 2018

Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control.
Proceedings of the 2018 European Control Conference, 2018

2017
Convolutional Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481).
Dagstuhl Reports, 2016

Understanding Probabilistic Sparse Gaussian Process Approximations.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Manifold Gaussian Processes for regression.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

2015
Gaussian Processes for Data-Efficient Learning in Robotics and Control.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

2014
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Variational Gaussian Process State-Space Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Policy search for learning robot control using sparse data.
Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

2013
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

2012
Robust Filtering and Smoothing with Gaussian Processes.
IEEE Trans. Automat. Contr., 2012

Gaussian Processes for time-marked time-series data.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Model based learning of sigma points in unscented Kalman filtering.
Neurocomputing, 2012

Active Learning of Model Evidence Using Bayesian Quadrature.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Modelling and control of nonlinear systems using Gaussian processes with partial model information.
Proceedings of the 51th IEEE Conference on Decision and Control, 2012

2011
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning.
Proceedings of the Robotics: Science and Systems VII, 2011

Gaussian Process Training with Input Noise.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Additive Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

PILCO: A Model-Based and Data-Efficient Approach to Policy Search.
Proceedings of the 28th International Conference on Machine Learning, 2011

Reinforcement learning with reference tracking control in continuous state spaces.
Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011

2010
State-Space Inference and Learning with Gaussian Processes.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Gaussian Processes for Machine Learning (GPML) Toolbox.
J. Mach. Learn. Res., 2010

Sparse Spectrum Gaussian Process Regression.
J. Mach. Learn. Res., 2010

Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution.
J. Comput. Sci. Technol., 2010

Gaussian Process Change Point Models.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Gaussian Mixture Modeling with Gaussian Process Latent Variable Models.
Proceedings of the Pattern Recognition, 2010

2009
Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures.
IEEE/ACM Trans. Comput. Biology Bioinform., 2009

Gaussian process dynamic programming.
Neurocomputing, 2009

2008
Probabilistic Inference for Fast Learning in Control.
Proceedings of the Recent Advances in Reinforcement Learning, 8th European Workshop, 2008

Model-Based Reinforcement Learning with Continuous States and Actions.
Proceedings of the ESANN 2008, 2008

2007
The Need for Open Source Software in Machine Learning.
J. Mach. Learn. Res., 2007

2006
A choice model with infinitely many latent features.
Proceedings of the Machine Learning, 2006

Gaussian processes for machine learning.
Adaptive computation and machine learning, MIT Press, ISBN: 026218253X, 2006

2005
Assessing Approximate Inference for Binary Gaussian Process Classification.
J. Mach. Learn. Res., 2005

A Unifying View of Sparse Approximate Gaussian Process Regression.
J. Mach. Learn. Res., 2005

Assessing Approximations for Gaussian Process Classification.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Evaluating Predictive Uncertainty Challenge.
Proceedings of the Machine Learning Challenges, 2005

Healing the relevance vector machine through augmentation.
Proceedings of the Machine Learning, 2005

2004
Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models.
Proceedings of the Biocomputing 2004, 2004

Learning Depth from Stereo.
Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

Modelling Spikes with Mixtures of Factor Analysers.
Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

Semi-supervised Kernel Regression Using Whitened Function Classes.
Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

2003
Warped Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Gaussian Processes in Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Prediction on Spike Data Using Kernel Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

Analysis of Some Methods for Reduced Rank Gaussian Process Regression.
Proceedings of the Switching and Learning in Feedback Systems, 2003

Gaussian Processes in Machine Learning.
Proceedings of the Advanced Lectures on Machine Learning, 2003

2002
Derivative Observations in Gaussian Process Models of Dynamic Systems.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Bayesian Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001
Infinite Mixtures of Gaussian Process Experts.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

The Infinite Hidden Markov Model.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

2000
Occam's Razor.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999
The Infinite Gaussian Mixture Model.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Bayesian Modelling of fMRI lime Series.
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1995
Gaussian Processes for Regression.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

A Practical Monte Carlo Implementation of Bayesian Learning.
Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994
Pruning from Adaptive Regularization.
Neural Computation, 1994

1993
Presynaptic and postsynaptic competition in models for the development of neuromuscular connections.
Biological Cybernetics, 1993


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