Carl E. Rasmussen

发表

A choice model with infinitely many latent features

Carl E. Rasmussen, Dilan Görür, Frank Jäkel, 2006, ICML.

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

Carl E. Rasmussen, Alessandro Davide Ialongo, Mark van der Wilk, 2018, ArXiv.

Semi-supervised Kernel Regression Using Whitened Function Classes

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

Prediction on Spike Data Using Kernel Algorithms

Carl E. Rasmussen, Bernhard Schölkopf, Jason Weston, 2003, NIPS.

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

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

State-Space Inference and Learning with Gaussian Processes

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

Gaussian Process Change Point Models

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

Probabilistic Inference for Fast Learning in Control

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

Modelling Spikes with Mixtures of Factor Analysers

Carl E. Rasmussen, Dilan Görür, Nikos K. Logothetis, 2004, DAGM-Symposium.

New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481)

Carl E. Rasmussen, Bernhard Schölkopf, Arthur Gretton, 2016, Dagstuhl Reports.

The Need for Open Source Software in Machine Learning

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

Gaussian Processes for Machine Learning (GPML) Toolbox

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

A Practical Monte Carlo Implementation of Bayesian Learning

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

A Unifying View of Sparse Approximate Gaussian Process Regression

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

Convolutional Gaussian Processes

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

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.

Active Learning of Model Evidence Using Bayesian Quadrature

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

Gaussian processes for machine learning

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

Assessing Approximate Inference for Binary Gaussian Process Classification

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

Derivative Observations in Gaussian Process Models of Dynamic Systems

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

Modelling and control of nonlinear systems using Gaussian processes with partial model information

Carl E. Rasmussen, Joseph Hall, Jan M. Maciejowski, 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

Gaussian Mixture Modeling with Gaussian Process Latent Variable Models

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

Infinite Mixtures of Gaussian Process Experts

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

Non-Factorised Variational Inference in Dynamical Systems pdf

Carl E. Rasmussen, James Hensman, Alessandro Davide Ialongo, 2018, ArXiv.

Robust Filtering and Smoothing with Gaussian Processes

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

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

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

Variational Gaussian Process State-Space Models

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

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.

Assessing Approximations for Gaussian Process Classification

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

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).

Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution

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

Policy search for learning robot control using sparse data

Carl E. Rasmussen, Alois Knoll, Jan Peters, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

The Infinite Gaussian Mixture Model

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

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.

Data-Efficient Reinforcement Learning in Continuous-State POMDPs pdf

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

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

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

Gaussian Processes for time-marked time-series data

Carl E. Rasmussen, Zoubin Ghahramani, John P. Cunningham, 2012, AISTATS.

Pruning from Adaptive Regularization

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

Bayesian Monte Carlo

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

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

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

Marginalised Gaussian Processes with Nested Sampling pdf

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

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

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

Model-Based Reinforcement Learning with Continuous States and Actions

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

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

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

PILCO: A Model-Based and Data-Efficient Approach to Policy Search

Carl E. Rasmussen, Marc Peter Deisenroth, M. Deisenroth, 2011, ICML.

Automated Bayesian System Identification with NARX Models pdf

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

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.

Sparse Spectrum Gaussian Process Regression

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

Bayesian Modelling of fMRI lime Series

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

Rates of Convergence for Sparse Variational Gaussian Process Regression

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

Healing the relevance vector machine through augmentation

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

Additive Gaussian Processes

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

Factorial Hidden Markov Models

Carl E. Rasmussen, Zoubin Ghahramani, Matthew J. Beal, 1997 .

Understanding Probabilistic Sparse Gaussian Process Approximations

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

Learning Depth from Stereo

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

Gaussian Processes in Reinforcement Learning

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

Gaussian process dynamic programming

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

Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

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

Deep Structured Mixtures of Gaussian Processes pdf

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

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.

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

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

Gaussian Process Training with Input Noise

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

Warped Gaussian Processes

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

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.

Evaluating Predictive Uncertainty Challenge

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

Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting

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

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

In Advances in Neural Information Processing Systems

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

Manifold Gaussian Processes for regression

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

The Promises and Pitfalls of Deep Kernel Learning

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

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 .