C. Rasmussen

Agathe Girard, Carl E. Rasmussen, Joaquin Quiñonero Candela, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

Gaussian process dynamic programming

Carl E. Rasmussen, Jan Peters, Marc Peter Deisenroth, 2009, Neurocomputing.

Gaussian processes for machine learning

Carl E. Rasmussen, Christopher K. I. Williams, C. Rasmussen, 2005, Adaptive computation and machine learning.

A Unifying View of Sparse Approximate Gaussian Process Regression

Carl E. Rasmussen, Joaquin Quiñonero Candela, C. Rasmussen, 2005, J. Mach. Learn. Res..

Observations on the Nyström Method for Gaussian Processes

Christopher K. I. Williams, Volker Tresp, C. Rasmussen, 2002 .

Multiple-step ahead prediction for non linear dynamic systems: A Gaussian Process treatment with propagation of the uncertainty

K. Obermayer, C. Rasmussen, A. Girard, 2003, NIPS 2003.

Gaussian Processes in Reinforcement Learning

Carl E. Rasmussen, Malte Kuss, C. Rasmussen, 2003, NIPS.

Probabilistic Inference for Fast Learning in Control

Carl E. Rasmussen, Marc Peter Deisenroth, M. Deisenroth, 2008, EWRL.

Bayesian Monte Carlo

Carl E. Rasmussen, Zoubin Ghahramani, Zoubin Ghahramani, 2002, NIPS.

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

Geoffrey E. Hinton, Carl Edward Rasmussen, C. Rasmussen, 1997 .

Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting

C. Rasmussen, A. Girard, J. Q. Candela, 2002, NIPS.

Gaussian Processes for Machine Learning (GPML) Toolbox

Carl E. Rasmussen, Hannes Nickisch, C. Rasmussen, 2010, J. Mach. Learn. Res..

Predictive control with Gaussian process models

J. Kocijan, R. Murray-Smith, C.E. Rasmussen, 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

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

Carl E. Rasmussen, Dieter Fox, Marc Peter Deisenroth, 2011, Robotics: Science and Systems.

Gaussian Processes for Data-Efficient Learning in Robotics and Control

Carl E. Rasmussen, Dieter Fox, Marc Peter Deisenroth, 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

In Advances in Neural Information Processing Systems

Carl E. Rasmussen, Christopher K. I. Williams, M. Hasselmo, 2011 .

Derivative Observations in Gaussian Process Models of Dynamic Systems

Carl E. Rasmussen, Douglas J. Leith, Roderick Murray-Smith, 2002, NIPS.

Adaptive regularization

L. K. Hansen, C. Rasmussen, J. Larsen, 1994, Proceedings of IEEE Workshop on Neural Networks for Signal Processing.

Adaptive, Cautious, Predictive control with Gaussian Process Priors

Agathe Girard, Roderick Murray-Smith, Daniel Sbarbaro, 2003 .

The Need for Open Source Software in Machine Learning

Carl E. Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, 2007, J. Mach. Learn. Res..

The Infinite Gaussian Mixture Model

Carl E. Rasmussen, C. Rasmussen, 1999, NIPS.

Approximate inference for robust Gaussian process regression

Malte Kuss, Lehel Csató, Carl Edward Rasmussen, 2005 .

Nonstationary Gaussian Process Regression using a Latent Extension of the Input Space

C. Rasmussen, Malte Kuss, T. Pfingsten, 2006 .

Learning Depth from Stereo

Carl E. Rasmussen, Joaquin Quiñonero Candela, Matthias O. Franz, 2004, DAGM-Symposium.

Gaussian Process Change Point Models

Carl E. Rasmussen, Yunus Saatci, Ryan D. Turner, 2010, ICML.

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

Carl E. Rasmussen, David J. Willshaw, D. Willshaw, 1993, Biological Cybernetics.

Infinite Mixtures of Gaussian Process Experts

Carl E. Rasmussen, Zoubin Ghahramani, Zoubin Ghahramani, 2001, NIPS.

Model based learning of sigma points in unscented Kalman filtering

Carl E. Rasmussen, Ryan D. Turner, C. Rasmussen, 2010, 2010 IEEE International Workshop on Machine Learning for Signal Processing.

Bayesian Modelling of fMRI lime Series

Carl E. Rasmussen, Lars Kai Hansen, Pedro A. d. F. R. Højen-Sørensen, 1999, NIPS.

Pruning from Adaptive Regularization

Carl E. Rasmussen, Lars Kai Hansen, L. K. Hansen, 1994, Neural Computation.

Healing the relevance vector machine through augmentation

Carl E. Rasmussen, Joaquin Quiñonero Candela, C. Rasmussen, 2005, ICML.

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

Carl E. Rasmussen, Fredrik Lindsten, Thomas B. Schön, 2013, NIPS.

Assessing Approximate Inference for Binary Gaussian Process Classification

Carl E. Rasmussen, Malte Kuss, C. Rasmussen, 2005, J. Mach. Learn. Res..

Active Learning of Model Evidence Using Bayesian Quadrature

Carl E. Rasmussen, Roman Garnett, Zoubin Ghahramani, 2012, NIPS.

Additive Gaussian Processes

Carl E. Rasmussen, Hannes Nickisch, David Duvenaud, 2011, NIPS.

Warped Gaussian Processes

Carl E. Rasmussen, Zoubin Ghahramani, Edward Snelson, 2003, NIPS.

Robust Filtering and Smoothing with Gaussian Processes

Carl E. Rasmussen, Uwe D. Hanebeck, Marco F. Huber, 2012, IEEE Transactions on Automatic Control.

Occam's Razor

Carl E. Rasmussen, Zoubin Ghahramani, Zoubin Ghahramani, 2000, NIPS.

Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals

A. P. Dawid, Michael A. West, James O. Berger, 2003 .

Sparse Spectrum Gaussian Process Regression

Carl E. Rasmussen, Joaquin Quiñonero Candela, Aníbal R. Figueiras-Vidal, 2010, J. Mach. Learn. Res..

Evaluating Predictive Uncertainty Challenge

Carl E. Rasmussen, Joaquin Quiñonero Candela, Bernhard Schölkopf, 2005, MLCW.

Prediction at an Uncertain Input for Gaussian Processes and Relevance Vector Machines Application to Multiple-Step Ahead Time-Series Forecasting

Agathe Girard, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, 2002 .

The delve manual

Geoffrey E. Hinton, Robert Tibshirani, Zoubin Ghahramani, 1996 .

Gaussian Process Training with Input Noise

Carl E. Rasmussen, Andrew McHutchon, C. Rasmussen, 2011, NIPS.

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

Carl E. Rasmussen, Mark van der Wilk, Yarin Gal, 2014, NIPS.

Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models

Carl E. Rasmussen, Zoubin Ghahramani, David L. Wild, 2003, Pacific Symposium on Biocomputing.

Assessing Approximations for Gaussian Process Classification

Carl E. Rasmussen, Malte Kuss, C. Rasmussen, 2005, NIPS.

Probabilistic Inference for Fast Learning in Control in Proceedings of the 8 th European Workshop on Reinforcement Learning ( EWRL

M. Deisenroth, C. Rasmussen, 2008 .

A Bayesian approach to modelling uncertainty in gene expression clusters

Zoubin Ghahramani, C. Rasmussen, D. Wild, 2002 .

Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution

Carl E. Rasmussen, Dilan Görür, C. Rasmussen, 2010, Journal of Computer Science and Technology.

Analysis of Some Methods for Reduced Rank Gaussian Process Regression

Carl E. Rasmussen, Joaquin Quiñonero Candela, C. Rasmussen, 2003, European Summer School on Multi-AgentControl.

Dirichlet process Gaussian mixture models: choice of the base distribution

Dilan Görür, Carl Edward Rasmussen, C. Rasmussen, 2010 .

Observations on the Nyström Method for Gaussian Process Prediction

Carl Edward Rasmussen, Cki Williams, A Scwaighofer, 2002 .

Automated Bayesian System Identification with NARX Models pdf

Carl E. Rasmussen, Roger Frigola, C. Rasmussen, 2013, ArXiv.

Deep Structured Mixtures of Gaussian Processes pdf

Carl E. Rasmussen, Franz Pernkopf, Robert Peharz, 2019, AISTATS.

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

Carl E. Rasmussen, Stephen J. Roberts, Jan M. Maciejowski, 2018, 2018 European Control Conference (ECC).

Carl Edward Rasmussen and Marc Peter Deisenroth Probabilistic Inference for Fast Learning in Control Chapter in Recent Advances in Reinforcement Learning , Lecture

M. Deisenroth, C. Rasmussen, 2008 .

Bayesian Machine Learning for the Prognosis of Combustion Instabilities From Noise

C. Rasmussen, M. Juniper, Ushnish Sengupta, 2020, Journal of Engineering for Gas Turbines and Power.

Max – Planck – Institut f ür biologische Kybernetik Max Planck Institute for Biological Cybernetics Technical Report No . 136 Approximate Inference for Robust Gaussian Process Regression

Carl E. Rasmussen, Malte Kuss, Lehel Csató, 2005 .

Approximating methods for intractable probabilistic models: Applications in neuroscience

Lars Kai Hansen, Jan Larsen, Pedro A. d. F. R. Højen-Sørensen, 2002 .

Perfusion quantification using Gaussian process deconvolution

C. Rasmussen, L. Hanson, H. Larsson, 2002, Magnetic resonance in medicine.

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

Carl E. Rasmussen, Jan Peters, Kenji Doya, 2019, ICML.

Bayesian modelling of tMRI time series

C. Rasmussen, Lars, Hansen, 2022 .

Lecture 6: Graphical Models: Learning

Zoubin Ghahramani, Carl Edward Rasmussen, Zoubin Ghahramani, 2010 .

Large-Scale Kernel Machines

Joaquin Quiñonero-Candela, Christopher K. I. Williams, Carl Edward Rasmussen, 2007 .

Marginalised Gaussian Processes with Nested Sampling pdf

Carl E. Rasmussen, Vidhi Lalchand, Fergus Simpson, 2020, NeurIPS.

Dirichlet Process Mixtures of Factor Analysers

C. Rasmussen, Dilan Görür, 2007 .

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

Mark van der Wilk, David R. Burt, C. Rasmussen, 2022, ArXiv.

Advances in Gaussian Processes Tutorial at NIPS 2006 in Vancouver

Carl Edward Rasmussen, C. Rasmussen, 2006 .

A case based comparison of identification with neural network and Gaussian process models.

Agathe Girard, J. Kocijan, Carl Edward Rasmussen, 2003 .

Document Models

Carl Edward Rasmussen, C. Rasmussen, 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

Sampling for non-conjugate infinite latent feature models

Carl Edward Rasmussen, Dilan Görür, J. M. Bernardo, 2006 .

Multiuser detection and channel estimation: Exact and approximate methods

Thomas Fabricius, Lars Kai Hansen, Carl Edward Rasmussen, 2003 .

Machine Learning Model Optimization with Hyper Parameter 1 Tuning Approach

F. Hutter, C. Rasmussen, S. Falkner, 2022 .

Causal and Probabilistic Inference Empirical and Theoretical Analysis of Bayesian Inference in Gaussian Process Models

C. Rasmussen, H. Nickisch, M. Seeger, 2009 .

Semi-supervised Kernel Regression Using Whitened Function Classes

Carl E. Rasmussen, Bernhard Schölkopf, Younghee Kwon, 2004, DAGM-Symposium.

A Practical and Conceptual Framework for Learning in Control

Marc Peter Deisenroth, Carl Edward Rasmussen, M. Deisenroth, 2010 .

Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

Carl E. Rasmussen, Hannes Nickisch, C. Rasmussen, 2010, DAGM-Symposium.

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

Carl E. Rasmussen, Zoubin Ghahramani, David L. Wild, 2009, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Rates of Convergence for Sparse Variational Gaussian Process Regression

Carl E. Rasmussen, Mark van der Wilk, David R. Burt, 2019, ICML.

A Practical Monte Carlo Implementation of Bayesian Learning

Carl E. Rasmussen, C. Rasmussen, 1995, NIPS.

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

Carl E. Rasmussen, James Hensman, Alessandro Davide Ialongo, 2019, ICML.

Manifold Gaussian Processes for regression

Carl E. Rasmussen, Jan Peters, Marc Peter Deisenroth, 2014, 2016 International Joint Conference on Neural Networks (IJCNN).

Model-Based Reinforcement Learning with Continuous States and Actions

Carl E. Rasmussen, Jan Peters, Marc Peter Deisenroth, 2008, ESANN.

Approximate dynamic programming with Gaussian processes

J. Peters, C.E. Rasmussen, M.P. Deisenroth, 2008, 2008 American Control Conference.

Data-Efficient Reinforcement Learning in Continuous-State POMDPs pdf

Carl E. Rasmussen, Rowan McAllister, C. Rasmussen, 2016, ArXiv.

The Promises and Pitfalls of Deep Kernel Learning

Carl E. Rasmussen, Mark van der Wilk, Sebastian W. Ober, 2021, UAI.

Datasets and Code

Christopher K. I. Williams, Carl Edward Rasmussen, C. Rasmussen, 2005 .

Understanding Probabilistic Sparse Gaussian Process Approximations

Carl E. Rasmussen, Mark van der Wilk, Matthias Bauer, 2016, NIPS.

Kernel Identification Through Transformers pdf

Alessandro Vullo, Nicolas Durrande, Vidhi Lalchand, 2021, NeurIPS.

Marginalised Gaussian Processes with Nested Sampling

C. Rasmussen, F. Simpson, V. Lalchand, 2020, NeurIPS.

Approximate Inference for Fully Bayesian Gaussian Process Regression pdf

Carl Edward Rasmussen, Vidhi Lalchand, C. Rasmussen, 2019, AABI.

Perfusion Quanti ation using Gaussian Pro essDe onvolutionI

C. Rasmussen, L. Hanson, H. Larsson, 2007 .

Ensembling geophysical models with Bayesian Neural Networks pdf

Carl Edward Rasmussen, Ushnish Sengupta, Matt Amos, 2020, NeurIPS.

Deep Structured Mixtures of Gaussian Processes

C. Rasmussen, M. Trapp, F. Pernkopf, 2019, AISTATS.

System Identification in Gaussian Process Dynamical Systems

Marc Peter Deisenroth, Ryan D. Turner, Carl Edward Rasmussen, 2009, NIPS 2009.

Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes

Carl E. Rasmussen, Roger Frigola, C. Rasmussen, 2013, 52nd IEEE Conference on Decision and Control.

Deep Convolutional Networks as shallow Gaussian Processes pdf

Laurence Aitchison, Carl Edward Rasmussen, Adria Garriga-Alonso, 2018, ICLR.

Variational Gaussian Process State-Space Models

Carl E. Rasmussen, Yutian Chen, Roger Frigola, 2014, NIPS.

Convergence of Sparse Variational Inference in Gaussian Processes Regression

Mark van der Wilk, Carl Edward Rasmussen, David R. Burt, 2020, J. Mach. Learn. Res..

State-Space Inference and Learning with Gaussian Processes

Carl E. Rasmussen, Marc Peter Deisenroth, Ryan D. Turner, 2010, AISTATS.

Benchmarking the Neural Linear Model for Regression pdf

Sebastian W. Ober, Carl Edward Rasmussen, C. Rasmussen, 2019, ArXiv.

Convolutional Gaussian Processes

Carl E. Rasmussen, James Hensman, Mark van der Wilk, 2017, NIPS.

Variational Orthogonal Features pdf

Mark van der Wilk, Carl Edward Rasmussen, David R. Burt, 2020, ArXiv.

Factorial Hidden Markov Models

Matthew J. Beal, Zoubin Ghahramani, C. Rasmussen, 2004, Machine Learning.

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

Carl E. Rasmussen, Fredrik Lindsten, Thomas B. Schön, 2013, ArXiv.

Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs

Carl E. Rasmussen, Rowan McAllister, C. Rasmussen, 2017, NIPS.

Gaussian Processes for Regression

Christopher K. I. Williams, C. Rasmussen, Carl E. Rasmussen, 1995, NIPS.

Reinforcement learning with reference tracking control in continuous state spaces

Carl E. Rasmussen, Joseph Hall, Jan M. Maciejowski, 2011, IEEE Conference on Decision and Control and European Control Conference.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

Michael I. Jordan, Tommi S. Jaakkola, Matthew J. Beal, 1996 .

Additive Gaussian Processes

D. Duvenaud, C. Rasmussen, H. Nickisch, 2011, NIPS.

The Promises and Pitfalls of Deep Kernel Learning

Mark van der Wilk, Sebastian W. Ober, C. Rasmussen, 2021, Conference on Uncertainty in Artificial Intelligence.

Probabilistic Independence Networks for Hidden Markov Probability Models

Michael I. Jordan, Padhraic Smyth, David Heckerman, 1997, Neural Computation.

Deep Convolutional Networks as shallow Gaussian Processes

C. Rasmussen, L. Aitchison, Adrià Garriga-Alonso, 2018, ICLR.

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

Carl E. Rasmussen, Franz Pernkopf, Robert Peharz, 2018, ArXiv.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

Michael I. Jordan, Tommi S. Jaakkola, Lawrence K. Saul, 1996 .

The GPML Toolbox version 4 . 2

Hannes Nickisch, Carl Edward Rasmussen, C. Rasmussen, 2016 .

Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees

Mark van der Wilk, David R. Burt, C. Rasmussen, 2022, ArXiv.

Sparse Gaussian Process Hyperparameters: Optimize or Integrate?

David R. Burt, C. Rasmussen, W. Bruinsma, 2022, NeurIPS.