Carl Edward Rasmussen

Publication Activity (10 Years)

Years Active: 1993-2024
Publications (10 Years): 35

Top Topics

Gaussian Process

Hyperparameters

Top Venues

Publications

Talay M. Cheema, Carl Edward Rasmussen
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes. Trans. Mach. Learn. Res. 2024 (2024)
Talay M. Cheema, Carl Edward Rasmussen
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes. CoRR (2023)
Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS (2022)
Alexander Terenin, David R. Burt, Artem Artemev, Seth Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR (2022)
Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen
Kernel Identification Through Transformers. NeurIPS (2021)
Yen-Chen Wu, Carl Edward Rasmussen
Clipping Loops for Sample-Efficient Dialogue Policy Optimisation. NAACL-HLT (2021)
Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Marginalised Gaussian Processes with Nested Sampling. NeurIPS (2021)
Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young
Ensembling geophysical models with Bayesian Neural Networks. NeurIPS (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR (2020)
Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen
Deep Structured Mixtures of Gaussian Processes. AISTATS (2020)
Jan-Peter Calliess, Stephen J. Roberts, Carl Edward Rasmussen, Jan M. Maciejowski
Lazily Adapted Constant Kinky Inference for nonparametric regression and model-reference adaptive control. Autom. 122 (2020)
Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young
Ensembling geophysical models with Bayesian Neural Networks. CoRR (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. 21 (2020)
Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
Marginalised Gaussian Processes with Nested Sampling. CoRR (2020)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Variational Orthogonal Features. CoRR (2020)
Yen-Chen Wu, Bo-Hsiang Tseng, Carl Edward Rasmussen
Improving Sample-Efficiency in Reinforcement Learning for Dialogue Systems by Using Trainable-Action-Mask. ICASSP (2020)
Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. CoRR (2019)
Vidhi Lalchand, Carl Edward Rasmussen
Approximate Inference for Fully Bayesian Gaussian Process Regression. AABI (2019)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML (2019)
Vidhi Lalchand, Carl Edward Rasmussen
Approximate Inference for Fully Bayesian Gaussian Process Regression. CoRR (2019)
Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison
Deep Convolutional Networks as shallow Gaussian Processes. ICLR (Poster) (2019)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR (2019)
David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML (2019)
Sebastian W. Ober, Carl Edward Rasmussen
Benchmarking the Neural Linear Model for Regression. CoRR (2019)
Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
Closed-form Inference and Prediction in Gaussian Process State-Space Models. CoRR (2018)
Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
Non-Factorised Variational Inference in Dynamical Systems. CoRR (2018)
Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. ICML (2018)
Adrià Garriga-Alonso, Laurence Aitchison, Carl Edward Rasmussen
Deep Convolutional Networks as shallow Gaussian Processes. CoRR (2018)
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Convolutional Gaussian Processes. NIPS (2017)
Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Convolutional Gaussian Processes. CoRR (2017)
Rowan McAllister, Carl Edward Rasmussen
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs. NIPS (2017)
Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth
Manifold Gaussian Processes for regression. IJCNN (2016)
Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf
New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). Dagstuhl Reports 6 (11) (2016)
Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
Understanding Probabilistic Sparse Gaussian Process Approximations. NIPS (2016)
Rowan McAllister, Carl Edward Rasmussen
Data-Efficient Reinforcement Learning in Continuous-State POMDPs. CoRR (2016)
Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen
Gaussian Processes for Data-Efficient Learning in Robotics and Control. CoRR (2015)
Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen
Gaussian Processes for Data-Efficient Learning in Robotics and Control. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth
Manifold Gaussian Processes for Regression. CoRR (2014)
Roger Frigola, Carl Edward Rasmussen
Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes. CDC (2013)
Roger Frigola, Carl Edward Rasmussen
Automated Bayesian System Identification with NARX Models CoRR (2013)
Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen
Robust Filtering and Smoothing with Gaussian Processes. IEEE Trans. Autom. Control. 57 (7) (2012)
Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski
Modelling and control of nonlinear systems using Gaussian processes with partial model information. CDC (2012)
Ryan D. Turner, Carl Edward Rasmussen
Model based learning of sigma points in unscented Kalman filtering. Neurocomputing 80 (2012)
Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen
Robust Filtering and Smoothing with Gaussian Processes CoRR (2012)
John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen
Gaussian Processes for time-marked time-series data. AISTATS (2012)
Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski
Reinforcement learning with reference tracking control in continuous state spaces. CDC/ECC (2011)
David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
Additive Gaussian Processes. NIPS (2011)
Marc Peter Deisenroth, Carl Edward Rasmussen
PILCO: A Model-Based and Data-Efficient Approach to Policy Search. ICML (2011)
Marc Peter Deisenroth, Carl Edward Rasmussen, Dieter Fox
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning. Robotics: Science and Systems (2011)
David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
Additive Gaussian Processes CoRR (2011)
Andrew McHutchon, Carl Edward Rasmussen
Gaussian Process Training with Input Noise. NIPS (2011)
Ryan D. Turner, Marc Peter Deisenroth, Carl Edward Rasmussen
State-Space Inference and Learning with Gaussian Processes. AISTATS (2010)
Carl Edward Rasmussen, Hannes Nickisch
Gaussian Processes for Machine Learning (GPML) Toolbox. J. Mach. Learn. Res. 11 (2010)
Hannes Nickisch, Carl Edward Rasmussen
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. DAGM-Symposium (2010)
Yunus Saatci, Ryan D. Turner, Carl Edward Rasmussen
Gaussian Process Change Point Models. ICML (2010)
Dilan Görür, Carl Edward Rasmussen
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution. J. Comput. Sci. Technol. 25 (4) (2010)
Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal
Sparse Spectrum Gaussian Process Regression. J. Mach. Learn. Res. 11 (2010)
Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters
Gaussian process dynamic programming. Neurocomputing 72 (7-9) (2009)
Carl Edward Rasmussen, Bernard J. de la Cruz, Zoubin Ghahramani, David L. Wild
Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures. IEEE ACM Trans. Comput. Biol. Bioinform. 6 (4) (2009)
Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters
Model-Based Reinforcement Learning with Continuous States and Actions. ESANN (2008)
Carl Edward Rasmussen, Marc Peter Deisenroth
Probabilistic Inference for Fast Learning in Control. EWRL (2008)
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
The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. 8 (2007)
Dilan Görür, Frank Jäkel, Carl Edward Rasmussen
A choice model with infinitely many latent features. ICML (2006)
Joaquin Quiñonero Candela, Carl Edward Rasmussen
A Unifying View of Sparse Approximate Gaussian Process Regression. J. Mach. Learn. Res. 6 (2005)
Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf
Evaluating Predictive Uncertainty Challenge. MLCW (2005)
Malte Kuss, Carl Edward Rasmussen
Assessing Approximations for Gaussian Process Classification. NIPS (2005)
Carl Edward Rasmussen, Joaquin Quiñonero Candela
Healing the relevance vector machine through augmentation. ICML (2005)
Malte Kuss, Carl Edward Rasmussen
Assessing Approximate Inference for Binary Gaussian Process Classification. J. Mach. Learn. Res. 6 (2005)
Matthias O. Franz, Younghee Kwon, Carl Edward Rasmussen, Bernhard Schölkopf
Semi-supervised Kernel Regression Using Whitened Function Classes. DAGM-Symposium (2004)
Dilan Görür, Carl Edward Rasmussen, Andreas S. Tolias, Fabian H. Sinz, Nikos K. Logothetis
Modelling Spikes with Mixtures of Factor Analysers. DAGM-Symposium (2004)
Fabian H. Sinz, Joaquin Quiñonero Candela, Gökhan H. Bakir, Carl Edward Rasmussen, Matthias O. Franz
Learning Depth from Stereo. DAGM-Symposium (2004)
Ananya Dubey, Seungwoo Hwang, Claudia Rangel, Carl Edward Rasmussen, Zoubin Ghahramani, David L. Wild
Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models. Pacific Symposium on Biocomputing (2004)
Joaquin Quiñonero Candela, Carl Edward Rasmussen
Analysis of Some Methods for Reduced Rank Gaussian Process Regression. European Summer School on Multi-AgentControl (2003)
Carl Edward Rasmussen
Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning (2003)
Carl Edward Rasmussen, Malte Kuss
Gaussian Processes in Reinforcement Learning. NIPS (2003)
Edward Lloyd Snelson, Carl Edward Rasmussen, Zoubin Ghahramani
Warped Gaussian Processes. NIPS (2003)
Jan Eichhorn, Andreas S. Tolias, Alexander Zien, Malte Kuss, Carl Edward Rasmussen, Jason Weston, Nikos K. Logothetis, Bernhard Schölkopf
Prediction on Spike Data Using Kernel Algorithms. NIPS (2003)
Joaquin Quiñonero Candela, Agathe Girard, Jan Larsen, Carl Edward Rasmussen
Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting. ICASSP (2) (2003)
Carl Edward Rasmussen, Zoubin Ghahramani
Bayesian Monte Carlo. NIPS (2002)
E. Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl Edward Rasmussen
Derivative Observations in Gaussian Process Models of Dynamic Systems. NIPS (2002)
Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith
Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting. NIPS (2002)
Carl Edward Rasmussen, Zoubin Ghahramani
Infinite Mixtures of Gaussian Process Experts. NIPS (2001)
Matthew J. Beal, Zoubin Ghahramani, Carl Edward Rasmussen
The Infinite Hidden Markov Model. NIPS (2001)
Carl Edward Rasmussen, Zoubin Ghahramani
Occam's Razor. NIPS (2000)
Pedro A. d. F. R. Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen
Bayesian Modelling of fMRI lime Series. NIPS (1999)
Carl Edward Rasmussen
The Infinite Gaussian Mixture Model. NIPS (1999)
Carl Edward Rasmussen
A Practical Monte Carlo Implementation of Bayesian Learning. NIPS (1995)
Christopher K. I. Williams, Carl Edward Rasmussen
Gaussian Processes for Regression. NIPS (1995)
Lars Kai Hansen, Carl Edward Rasmussen
Pruning from Adaptive Regularization. Neural Comput. 6 (6) (1994)
Carl Edward Rasmussen, David J. Willshaw
Presynaptic and postsynaptic competition in models for the development of neuromuscular connections. Biol. Cybern. 68 (5) (1993)