Carl E. Rasmussen

According to our database, Carl E. Rasmussen authored at least 90 papers between 1993 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes.

Talay M. Cheema, Carl Edward Rasmussen

CoRR, 2023

2022

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees.

Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge

CoRR, 2022

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

The promises and pitfalls of deep kernel learning.

Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Marginalised Gaussian Processes with Nested Sampling.

Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Kernel Identification Through Transformers.

Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Clipping Loops for Sample-Efficient Dialogue Policy Optimisation.

Yen-Chen Wu, Carl Edward Rasmussen

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

2020

Convergence of Sparse Variational Inference in Gaussian Processes Regression.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

J. Mach. Learn. Res., 2020

Variational Orthogonal Features.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

CoRR, 2020

Lazily Adapted Constant Kinky Inference for nonparametric regression and model-reference adaptive control.

Jan-Peter Calliess, Stephen J. Roberts, Carl Edward Rasmussen, Jan M. Maciejowski

Autom., 2020

Ensembling geophysical models with Bayesian Neural Networks.

Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Improving Sample-Efficiency in Reinforcement Learning for Dialogue Systems by Using Trainable-Action-Mask.

Yen-Chen Wu, Bo-Hsiang Tseng, Carl Edward Rasmussen

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Deep Structured Mixtures of Gaussian Processes.

Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Benchmarking the Neural Linear Model for Regression.

Sebastian W. Ober, Carl Edward Rasmussen

CoRR, 2019

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models.

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

Proceedings of the 36th International Conference on Machine Learning, 2019

Rates of Convergence for Sparse Variational Gaussian Process Regression.

David R. Burt, Carl Edward Rasmussen, Mark van der Wilk

Proceedings of the 36th International Conference on Machine Learning, 2019

Deep Convolutional Networks as shallow Gaussian Processes.

Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison

Proceedings of the 7th International Conference on Learning Representations, 2019

Approximate Inference for Fully Bayesian Gaussian Process Regression.

Vidhi Lalchand, Carl Edward Rasmussen

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

Non-Factorised Variational Inference in Dynamical Systems.

Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen

CoRR, 2018

Closed-form Inference and Prediction in Gaussian Process State-Space Models.

Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen

CoRR, 2018

Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks.

Martin Trapp, Robert Peharz, Carl E. Rasmussen, Franz Pernkopf

CoRR, 2018

PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos.

Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya

Proceedings of the 35th International Conference on Machine Learning, 2018

Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control.

Jan-Peter Calliess, Stephen J. Roberts, Carl E. Rasmussen, Jan M. Maciejowski

Proceedings of the 16th European Control Conference, 2018

2017

Convolutional Gaussian Processes.

Mark van der Wilk, Carl Edward Rasmussen, James Hensman

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.

Rowan McAllister, Carl Edward Rasmussen

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).

Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf

Dagstuhl Reports, 2016

Data-Efficient Reinforcement Learning in Continuous-State POMDPs.

Rowan McAllister, Carl Edward Rasmussen

CoRR, 2016

Understanding Probabilistic Sparse Gaussian Process Approximations.

Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Manifold Gaussian Processes for regression.

Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

2015

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen

IEEE Trans. Pattern Anal. Mach. Intell., 2015

2014

Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.

Yarin Gal, Mark van der Wilk, Carl E. Rasmussen

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.

Roger Frigola, Yutian Chen, Carl E. Rasmussen

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.

Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois C. Knoll, Jan Peters, Marc Peter Deisenroth

Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

2013

Automated Bayesian System Identification with NARX Models

Roger Frigola, Carl Edward Rasmussen

CoRR, 2013

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM.

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

CoRR, 2013

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen

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.

Roger Frigola, Carl Edward Rasmussen

Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

2012

Robust Filtering and Smoothing with Gaussian Processes.

Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen

IEEE Trans. Autom. Control., 2012

Gaussian Processes for time-marked time-series data.

John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Model based learning of sigma points in unscented Kalman filtering.

Ryan D. Turner, Carl Edward Rasmussen

Neurocomputing, 2012

Active Learning of Model Evidence Using Bayesian Quadrature.

Michael A. Osborne, David Duvenaud, Roman Garnett, Carl E. Rasmussen, Stephen J. Roberts, Zoubin Ghahramani

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.

Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski

Proceedings of the 51th IEEE Conference on Decision and Control, 2012

2011

Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning.

Marc Peter Deisenroth, Carl Edward Rasmussen, Dieter Fox

Proceedings of the Robotics: Science and Systems VII, 2011

Gaussian Process Training with Input Noise.

Andrew McHutchon, Carl Edward Rasmussen

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.

David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen

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.

Marc Peter Deisenroth, Carl Edward Rasmussen

Proceedings of the 28th International Conference on Machine Learning, 2011

Reinforcement learning with reference tracking control in continuous state spaces.

Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski

Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, 2011

2010

State-Space Inference and Learning with Gaussian Processes.

Ryan D. Turner, Marc Peter Deisenroth, Carl Edward Rasmussen

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Gaussian Processes for Machine Learning (GPML) Toolbox.

Carl Edward Rasmussen, Hannes Nickisch

J. Mach. Learn. Res., 2010

Sparse Spectrum Gaussian Process Regression.

Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal

J. Mach. Learn. Res., 2010

Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution.

Dilan Görür, Carl Edward Rasmussen

J. Comput. Sci. Technol., 2010

Gaussian Process Change Point Models.

Yunus Saatci, Ryan D. Turner, Carl Edward Rasmussen

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Gaussian Mixture Modeling with Gaussian Process Latent Variable Models.

Hannes Nickisch, Carl Edward Rasmussen

Proceedings of the Pattern Recognition, 2010

2009

Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures.

Carl Edward Rasmussen, Bernard J. de la Cruz, Zoubin Ghahramani, David L. Wild

IEEE ACM Trans. Comput. Biol. Bioinform., 2009

Gaussian process dynamic programming.

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters

Neurocomputing, 2009

2008

Probabilistic Inference for Fast Learning in Control.

Carl Edward Rasmussen, Marc Peter Deisenroth

Proceedings of the Recent Advances in Reinforcement Learning, 8th European Workshop, 2008

Model-Based Reinforcement Learning with Continuous States and Actions.

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

Approximate dynamic programming with Gaussian processes.

Marc Peter Deisenroth, Jan Peters, Carl E. Rasmussen

Proceedings of the American Control Conference, 2008

2007

The Need for Open Source Software in Machine Learning.

Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson

J. Mach. Learn. Res., 2007

2006

A choice model with infinitely many latent features.

Dilan Görür, Frank Jäkel, Carl Edward Rasmussen

Proceedings of the Machine Learning, 2006

Gaussian processes for machine learning.

Carl Edward Rasmussen, Christopher K. I. Williams

Adaptive computation and machine learning, MIT Press, ISBN: 026218253X, 2006

2005

Assessing Approximate Inference for Binary Gaussian Process Classification.

Malte Kuss, Carl Edward Rasmussen

J. Mach. Learn. Res., 2005

A Unifying View of Sparse Approximate Gaussian Process Regression.

Joaquin Quiñonero Candela, Carl Edward Rasmussen

J. Mach. Learn. Res., 2005

Assessing Approximations for Gaussian Process Classification.

Malte Kuss, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Evaluating Predictive Uncertainty Challenge.

Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf

Proceedings of the Machine Learning Challenges, 2005

Healing the relevance vector machine through augmentation.

Carl Edward Rasmussen, Joaquin Quiñonero Candela

Proceedings of the Machine Learning, 2005

2004

Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models.

Ananya Dubey, Seungwoo Hwang, Claudia Rangel, Carl Edward Rasmussen, Zoubin Ghahramani, David L. Wild

Proceedings of the Biocomputing 2004, 2004

Learning Depth from Stereo.

Fabian H. Sinz, Joaquin Quiñonero Candela, Gökhan H. Bakir, Carl Edward Rasmussen, Matthias O. Franz

Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

Modelling Spikes with Mixtures of Factor Analysers.

Dilan Görür, Carl Edward Rasmussen, Andreas S. Tolias, Fabian H. Sinz, Nikos K. Logothetis

Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

Semi-supervised Kernel Regression Using Whitened Function Classes.

Matthias O. Franz, Younghee Kwon, Carl Edward Rasmussen, Bernhard Schölkopf

Proceedings of the Pattern Recognition, 26th DAGM Symposium, August 30, 2004

Gaussian process model based predictive control.

Jus Kocijan, Roderick Murray-Smith, Carl E. Rasmussen, Agathe Girard

Proceedings of the 2004 American Control Conference, 2004

2003

Warped Gaussian Processes.

Edward Lloyd Snelson, Carl Edward Rasmussen, Zoubin Ghahramani

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Gaussian Processes in Reinforcement Learning.

Carl Edward Rasmussen, Malte Kuss

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Prediction on Spike Data Using Kernel Algorithms.

Jan Eichhorn, Andreas S. Tolias, Alexander Zien, Malte Kuss, Carl Edward Rasmussen, Jason Weston, Nikos K. Logothetis, Bernhard Schölkopf

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.

Joaquin Quiñonero Candela, Agathe Girard, Jan Larsen, Carl Edward Rasmussen

Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

Analysis of Some Methods for Reduced Rank Gaussian Process Regression.

Joaquin Quiñonero Candela, Carl Edward Rasmussen

Proceedings of the Switching and Learning in Feedback Systems, 2003

Gaussian Processes in Machine Learning.

Carl Edward Rasmussen

Proceedings of the Advanced Lectures on Machine Learning, 2003

2002

Derivative Observations in Gaussian Process Models of Dynamic Systems.

E. Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Bayesian Monte Carlo.

Carl Edward Rasmussen, Zoubin Ghahramani

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.

Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Infinite Mixtures of Gaussian Process Experts.

Carl Edward Rasmussen, Zoubin Ghahramani

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

The Infinite Hidden Markov Model.

Matthew J. Beal, Zoubin Ghahramani, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

2000

Occam's Razor.

Carl Edward Rasmussen, Zoubin Ghahramani

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999

The Infinite Gaussian Mixture Model.

Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Bayesian Modelling of fMRI lime Series.

Pedro A. d. F. R. Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1997

Evaluation of Gaussian processes and other methods for non-linear regression.

Carl E. Rasmussen

PhD thesis, 1997

1995

Gaussian Processes for Regression.

Christopher K. I. Williams, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

A Practical Monte Carlo Implementation of Bayesian Learning.

Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994

Pruning from Adaptive Regularization.

Lars Kai Hansen, Carl Edward Rasmussen

Neural Comput., 1994

1993

Presynaptic and postsynaptic competition in models for the development of neuromuscular connections.

Carl Edward Rasmussen, David J. Willshaw

Biol. Cybern., 1993