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ORCIDCarl E. Rasmussen
Carl Edward Rasmussen
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- affiliation: University of Cambridge, Department of Engineering, UK
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2020 – today
- 2024
[j17]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. 25: 26:1-26:36 (2024)- [j16]Talay M. Cheema, Carl Edward Rasmussen:
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes. Trans. Mach. Learn. Res. 2024 (2024) - 2023
- [i31]Talay M. Cheema, Carl Edward Rasmussen:
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes. CoRR abs/2308.14142 (2023) - 2022
- [c62]Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen:
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS 2022 - [i30]Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
:
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR abs/2210.07893 (2022) - [i29]Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl E. Rasmussen:
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? CoRR abs/2211.02476 (2022) - 2021
- [c61]Yen-Chen Wu, Carl Edward Rasmussen:
Clipping Loops for Sample-Efficient Dialogue Policy Optimisation. NAACL-HLT 2021: 3420-3428 - [c60]Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen:
Kernel Identification Through Transformers. NeurIPS 2021: 10483-10495 - [c59]Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen:
Marginalised Gaussian Processes with Nested Sampling. NeurIPS 2021: 13613-13625 - [c58]Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk:
The promises and pitfalls of deep kernel learning. UAI 2021: 1206-1216 - [i28]Sebastian W. Ober, Carl E. Rasmussen, Mark van der Wilk:
The Promises and Pitfalls of Deep Kernel Learning. CoRR abs/2102.12108 (2021) - [i27]Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl E. Rasmussen:
Kernel Identification Through Transformers. CoRR abs/2106.08185 (2021) - 2020
- [j15]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: 109216 (2020) - [j14]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. 21: 131:1-131:63 (2020) - [c57]Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen:
Deep Structured Mixtures of Gaussian Processes. AISTATS 2020: 2251-2261 - [c56]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: 8024-8028 - [c55]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 - [i26]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Variational Orthogonal Features. CoRR abs/2006.13170 (2020) - [i25]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR abs/2008.00323 (2020) - [i24]Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young:
Ensembling geophysical models with Bayesian Neural Networks. CoRR abs/2010.03561 (2020) - [i23]Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen:
Marginalised Gaussian Processes with Nested Sampling. CoRR abs/2010.16344 (2020)
2010 – 2019
- 2019
- [c54]Vidhi Lalchand, Carl Edward Rasmussen:
Approximate Inference for Fully Bayesian Gaussian Process Regression. AABI 2019: 1-12 - [c53]Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison:
Deep Convolutional Networks as shallow Gaussian Processes. ICLR (Poster) 2019 - [c52]David R. Burt, Carl Edward Rasmussen, Mark van der Wilk:
Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML 2019: 862-871 - [c51]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML 2019: 2931-2940 - [i22]Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya:
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. CoRR abs/1902.01240 (2019) - [i21]David R. Burt, Carl E. Rasmussen, Mark van der Wilk:
Rates of Convergence for Sparse Variational Gaussian Process Regression. CoRR abs/1903.03571 (2019) - [i20]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR abs/1906.05828 (2019) - [i19]Martin Trapp, Robert Peharz, Franz Pernkopf, Carl E. Rasmussen:
Deep Structured Mixtures of Gaussian Processes. CoRR abs/1910.04536 (2019) - [i18]Sebastian W. Ober, Carl Edward Rasmussen:
Benchmarking the Neural Linear Model for Regression. CoRR abs/1912.08416 (2019) - [i17]Vidhi Lalchand, Carl Edward Rasmussen:
Approximate Inference for Fully Bayesian Gaussian Process Regression. CoRR abs/1912.13440 (2019) - 2018
- [c50]Jan-Peter Calliess, Stephen J. Roberts, Carl E. Rasmussen, Jan M. Maciejowski:
Nonlinear Set Membership Regression with Adaptive Hyper-Parameter Estimation for Online Learning and Control. ECC 2018: 1-6 - [c49]Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya:
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. ICML 2018: 4062-4071 - [i16]Adrià Garriga-Alonso, Laurence Aitchison, Carl Edward Rasmussen:
Deep Convolutional Networks as shallow Gaussian Processes. CoRR abs/1808.05587 (2018) - [i15]Martin Trapp, Robert Peharz, Carl E. Rasmussen, Franz Pernkopf:
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks. CoRR abs/1809.04400 (2018) - [i14]Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen:
Closed-form Inference and Prediction in Gaussian Process State-Space Models. CoRR abs/1812.03580 (2018) - [i13]Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen:
Non-Factorised Variational Inference in Dynamical Systems. CoRR abs/1812.06067 (2018) - 2017
- [c48]Rowan McAllister, Carl Edward Rasmussen:
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs. NIPS 2017: 2040-2049 - [c47]Mark van der Wilk, Carl Edward Rasmussen, James Hensman:
Convolutional Gaussian Processes. NIPS 2017: 2849-2858 - [i12]Mark van der Wilk, Carl Edward Rasmussen, James Hensman:
Convolutional Gaussian Processes. CoRR abs/1709.01894 (2017) - 2016
- [c46]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for regression. IJCNN 2016: 3338-3345 - [c45]Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen:
Understanding Probabilistic Sparse Gaussian Process Approximations. NIPS 2016: 1525-1533 - [i11]Rowan McAllister, Carl Edward Rasmussen:
Data-Efficient Reinforcement Learning in Continuous-State POMDPs. CoRR abs/1602.02523 (2016) - [i10]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): 142-167 (2016) - 2015
- [j13]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): 408-423 (2015) - [i9]Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen:
Gaussian Processes for Data-Efficient Learning in Robotics and Control. CoRR abs/1502.02860 (2015) - 2014
- [c44]Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois C. Knoll, Jan Peters, Marc Peter Deisenroth:
Policy search for learning robot control using sparse data. ICRA 2014: 3882-3887 - [c43]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. NIPS 2014: 3257-3265 - [c42]Roger Frigola, Yutian Chen, Carl E. Rasmussen:
Variational Gaussian Process State-Space Models. NIPS 2014: 3680-3688 - [i8]Yarin Gal, Mark van der Wilk, Carl E. Rasmussen:
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models. CoRR abs/1402.1389 (2014) - [i7]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for Regression. CoRR abs/1402.5876 (2014) - [i6]Roger Frigola, Yutian Chen, Carl E. Rasmussen:
Variational Gaussian Process State-Space Models. CoRR abs/1406.4905 (2014) - 2013
- [c41]Roger Frigola, Carl Edward Rasmussen:
Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes. CDC 2013: 5371-5376 - [c40]Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen:
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. NIPS 2013: 3156-3164 - [i5]Roger Frigola, Carl Edward Rasmussen:
Automated Bayesian System Identification with NARX Models. CoRR abs/1303.2912 (2013) - [i4]Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen:
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. CoRR abs/1306.2861 (2013) - [i3]Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen:
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM. CoRR abs/1312.4852 (2013) - 2012
- [j12]Ryan D. Turner, Carl Edward Rasmussen:
Model based learning of sigma points in unscented Kalman filtering. Neurocomputing 80: 47-53 (2012) - [j11]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): 1865-1871 (2012) - [c39]Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski:
Modelling and control of nonlinear systems using Gaussian processes with partial model information. CDC 2012: 5266-5271 - [c38]Michael A. Osborne, David Duvenaud, Roman Garnett, Carl E. Rasmussen, Stephen J. Roberts, Zoubin Ghahramani:
Active Learning of Model Evidence Using Bayesian Quadrature. NIPS 2012: 46-54 - [c37]John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen:
Gaussian Processes for time-marked time-series data. AISTATS 2012: 255-263 - [i2]Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen:
Robust Filtering and Smoothing with Gaussian Processes. CoRR abs/1203.4345 (2012) - 2011
- [c36]Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski:
Reinforcement learning with reference tracking control in continuous state spaces. CDC/ECC 2011: 6019-6024 - [c35]Marc Peter Deisenroth, Carl Edward Rasmussen:
PILCO: A Model-Based and Data-Efficient Approach to Policy Search. ICML 2011: 465-472 - [c34]David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen:
Additive Gaussian Processes. NIPS 2011: 226-234 - [c33]Andrew McHutchon, Carl Edward Rasmussen:
Gaussian Process Training with Input Noise. NIPS 2011: 1341-1349 - [c32]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 - [i1]David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen:
Additive Gaussian Processes. CoRR abs/1112.4394 (2011) - 2010
- [j10]Dilan Görür, Carl Edward Rasmussen:
Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution. J. Comput. Sci. Technol. 25(4): 653-664 (2010) - [j9]Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal:
Sparse Spectrum Gaussian Process Regression. J. Mach. Learn. Res. 11: 1865-1881 (2010) - [j8]Carl Edward Rasmussen, Hannes Nickisch:
Gaussian Processes for Machine Learning (GPML) Toolbox. J. Mach. Learn. Res. 11: 3011-3015 (2010) - [c31]Hannes Nickisch, Carl Edward Rasmussen:
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. DAGM-Symposium 2010: 272-282 - [c30]Yunus Saatci, Ryan D. Turner, Carl Edward Rasmussen:
Gaussian Process Change Point Models. ICML 2010: 927-934 - [c29]Ryan D. Turner, Marc Peter Deisenroth, Carl Edward Rasmussen:
State-Space Inference and Learning with Gaussian Processes. AISTATS 2010: 868-875
2000 – 2009
- 2009
- [j7]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Gaussian process dynamic programming. Neurocomputing 72(7-9): 1508-1524 (2009) - [j6]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): 615-628 (2009) - 2008
- [c28]Marc Peter Deisenroth, Jan Peters, Carl E. Rasmussen:
Approximate dynamic programming with Gaussian processes. ACC 2008: 4480-4485 - [c27]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Model-Based Reinforcement Learning with Continuous States and Actions. ESANN 2008: 19-24 - [c26]Carl Edward Rasmussen, Marc Peter Deisenroth:
Probabilistic Inference for Fast Learning in Control. EWRL 2008: 229-242 - 2007
- [j5]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: 2443-2466 (2007) - 2006
- [b2]Carl Edward Rasmussen, Christopher K. I. Williams:
Gaussian processes for machine learning. Adaptive computation and machine learning, MIT Press 2006, ISBN 026218253X, pp. I-XVIII, 1-248 - [c25]Dilan Görür, Frank Jäkel, Carl Edward Rasmussen:
A choice model with infinitely many latent features. ICML 2006: 361-368 - 2005
- [j4]Malte Kuss, Carl Edward Rasmussen:
Assessing Approximate Inference for Binary Gaussian Process Classification. J. Mach. Learn. Res. 6: 1679-1704 (2005) - [j3]Joaquin Quiñonero Candela, Carl Edward Rasmussen:
A Unifying View of Sparse Approximate Gaussian Process Regression. J. Mach. Learn. Res. 6: 1939-1959 (2005) - [c24]Carl Edward Rasmussen, Joaquin Quiñonero Candela:
Healing the relevance vector machine through augmentation. ICML 2005: 689-696 - [c23]Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf:
Evaluating Predictive Uncertainty Challenge. MLCW 2005: 1-27 - [c22]Malte Kuss, Carl Edward Rasmussen:
Assessing Approximations for Gaussian Process Classification. NIPS 2005: 699-706 - 2004
- [c21]Jus Kocijan, Roderick Murray-Smith, Carl E. Rasmussen, Agathe Girard:
Gaussian process model based predictive control. ACC 2004: 2214-2219 - [c20]Matthias O. Franz, Younghee Kwon, Carl Edward Rasmussen, Bernhard Schölkopf:
Semi-supervised Kernel Regression Using Whitened Function Classes. DAGM-Symposium 2004: 18-26 - [c19]Fabian H. Sinz, Joaquin Quiñonero Candela, Gökhan H. Bakir, Carl Edward Rasmussen, Matthias O. Franz:
Learning Depth from Stereo. DAGM-Symposium 2004: 245-252 - [c18]Dilan Görür, Carl Edward Rasmussen, Andreas S. Tolias, Fabian H. Sinz, Nikos K. Logothetis:
Modelling Spikes with Mixtures of Factor Analysers. DAGM-Symposium 2004: 391-398 - [c17]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: 399-410 - [e1]Carl Edward Rasmussen, Heinrich H. Bülthoff, Bernhard Schölkopf, Martin A. Giese:
Pattern Recognition, 26th DAGM Symposium, August 30 - September 1, 2004, Tübingen, Germany, Proceedings. Lecture Notes in Computer Science 3175, Springer 2004, ISBN 3-540-22945-0 [contents] - 2003
- [c16]Carl Edward Rasmussen:
Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning 2003: 63-71 - [c15]Joaquin Quiñonero Candela, Carl Edward Rasmussen:
Analysis of Some Methods for Reduced Rank Gaussian Process Regression. European Summer School on Multi-AgentControl 2003: 98-127 - [c14]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: 701-704 - [c13]Edward Lloyd Snelson, Carl Edward Rasmussen, Zoubin Ghahramani:
Warped Gaussian Processes. NIPS 2003: 337-344 - [c12]Carl Edward Rasmussen, Malte Kuss:
Gaussian Processes in Reinforcement Learning. NIPS 2003: 751-758 - [c11]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: 1367-1374 - 2002
- [c10]Carl Edward Rasmussen, Zoubin Ghahramani:
Bayesian Monte Carlo. NIPS 2002: 489-496 - [c9]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: 529-536 - [c8]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: 1033-1040 - 2001
- [c7]Matthew J. Beal, Zoubin Ghahramani, Carl Edward Rasmussen:
The Infinite Hidden Markov Model. NIPS 2001: 577-584 - [c6]Carl Edward Rasmussen, Zoubin Ghahramani:
Infinite Mixtures of Gaussian Process Experts. NIPS 2001: 881-888 - 2000
- [c5]Carl Edward Rasmussen, Zoubin Ghahramani:
Occam's Razor. NIPS 2000: 294-300
1990 – 1999
- 1999
- [c4]Carl Edward Rasmussen:
The Infinite Gaussian Mixture Model. NIPS 1999: 554-560 - [c3]Pedro A. d. F. R. Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen:
Bayesian Modelling of fMRI lime Series. NIPS 1999: 754-760 - 1997
- [b1]Carl E. Rasmussen:
Evaluation of Gaussian processes and other methods for non-linear regression. University of Toronto, Canada, 1997 - 1995
- [c2]Christopher K. I. Williams, Carl Edward Rasmussen:
Gaussian Processes for Regression. NIPS 1995: 514-520 - [c1]Carl Edward Rasmussen:
A Practical Monte Carlo Implementation of Bayesian Learning. NIPS 1995: 598-604 - 1994
- [j2]Lars Kai Hansen, Carl Edward Rasmussen:
Pruning from Adaptive Regularization. Neural Comput. 6(6): 1223-1232 (1994) - 1993
- [j1]Carl Edward Rasmussen, David J. Willshaw:
Presynaptic and postsynaptic competition in models for the development of neuromuscular connections. Biol. Cybern. 68(5): 409-419 (1993)
Coauthor Index
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