Carl Edward Rasmussen

94 papers A* 11A 3B 2Misc 2Journal 39Unranked 35

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	ExoPredicator: Learning Abstract Models of Dynamic Worlds for Robot Planning. CoRR Yichao Liang, Dat Nguyen, Cambridge Yang, Tianyang Li, Joshua B. Tenenbaum, Carl Edward Rasmussen, Adrian Weller, Zenna Tavares, Tom Silver, Kevin Ellis
2024	J	`jnl`	Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes. Trans. Mach. Learn. Res. Talay M. Cheema, Carl Edward Rasmussen
2024	J	`jnl`	Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. J. Mach. Learn. Res. Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
2023	J	`jnl`	Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes. CoRR Talay M. Cheema, Carl Edward Rasmussen
2022	J	`jnl`	Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees. CoRR Alexander Terenin, David R. Burt, Artem Artemev, Seth R. Flaxman, Mark van der Wilk, Carl Edward Rasmussen, Hong Ge
2022	A*	`conf`	Sparse Gaussian Process Hyperparameters: Optimize or Integrate? NeurIPS Vidhi Lalchand, Wessel P. Bruinsma, David R. Burt, Carl Edward Rasmussen
2021	—	`conf`	Clipping Loops for Sample-Efficient Dialogue Policy Optimisation. NAACL-HLT Yen-Chen Wu, Carl Edward Rasmussen
2021	A*	`conf`	Kernel Identification Through Transformers. NeurIPS Fergus Simpson, Ian Davies, Vidhi Lalchand, Alessandro Vullo, Nicolas Durrande, Carl Edward Rasmussen
2021	A*	`conf`	Marginalised Gaussian Processes with Nested Sampling. NeurIPS Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
2020	J	`jnl`	Convergence of Sparse Variational Inference in Gaussian Processes Regression. CoRR David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2020	J	`jnl`	Convergence of Sparse Variational Inference in Gaussian Processes Regression. J. Mach. Learn. Res. David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2020	A	`conf`	Deep Structured Mixtures of Gaussian Processes. AISTATS Martin Trapp, Robert Peharz, Franz Pernkopf, Carl Edward Rasmussen
2020	A*	`conf`	Ensembling geophysical models with Bayesian Neural Networks. NeurIPS Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young
2020	J	`jnl`	Ensembling geophysical models with Bayesian Neural Networks. CoRR Ushnish Sengupta, Matt Amos, J. Scott Hosking, Carl Edward Rasmussen, Matthew P. Juniper, Paul J. Young
2020	Misc	`conf`	Improving Sample-Efficiency in Reinforcement Learning for Dialogue Systems by Using Trainable-Action-Mask. ICASSP Yen-Chen Wu, Bo-Hsiang Tseng, Carl Edward Rasmussen
2020	J	`jnl`	Lazily Adapted Constant Kinky Inference for nonparametric regression and model-reference adaptive control. Autom. Jan-Peter Calliess, Stephen J. Roberts, Carl Edward Rasmussen, Jan M. Maciejowski
2020	J	`jnl`	Marginalised Gaussian Processes with Nested Sampling. CoRR Fergus Simpson, Vidhi Lalchand, Carl Edward Rasmussen
2020	J	`jnl`	Variational Orthogonal Features. CoRR David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2019	—	`conf`	Approximate Inference for Fully Bayesian Gaussian Process Regression. AABI Vidhi Lalchand, Carl Edward Rasmussen
2019	J	`jnl`	Approximate Inference for Fully Bayesian Gaussian Process Regression. CoRR Vidhi Lalchand, Carl Edward Rasmussen
2019	J	`jnl`	Benchmarking the Neural Linear Model for Regression. CoRR Sebastian W. Ober, Carl Edward Rasmussen
2019	—	`conf`	Deep Convolutional Networks as shallow Gaussian Processes. ICLR (Poster) Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison
2019	A*	`conf`	Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. ICML Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
2019	J	`jnl`	Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models. CoRR Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
2019	J	`jnl`	PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. CoRR Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya
2019	A*	`conf`	Rates of Convergence for Sparse Variational Gaussian Process Regression. ICML David R. Burt, Carl Edward Rasmussen, Mark van der Wilk
2018	J	`jnl`	Closed-form Inference and Prediction in Gaussian Process State-Space Models. CoRR Alessandro Davide Ialongo, Mark van der Wilk, Carl Edward Rasmussen
2018	J	`jnl`	Deep Convolutional Networks as shallow Gaussian Processes. CoRR Adrià Garriga-Alonso, Laurence Aitchison, Carl Edward Rasmussen
2018	J	`jnl`	Non-Factorised Variational Inference in Dynamical Systems. CoRR Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
2018	A*	`conf`	PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos. ICML Paavo Parmas, Carl Edward Rasmussen, Jan Peters, Kenji Doya
2017	—	`conf`	Convolutional Gaussian Processes. NIPS Mark van der Wilk, Carl Edward Rasmussen, James Hensman
2017	J	`jnl`	Convolutional Gaussian Processes. CoRR Mark van der Wilk, Carl Edward Rasmussen, James Hensman
2017	—	`conf`	Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs. NIPS Rowan McAllister, Carl Edward Rasmussen
2016	J	`jnl`	Data-Efficient Reinforcement Learning in Continuous-State POMDPs. CoRR Rowan McAllister, Carl Edward Rasmussen
2016	B	`conf`	Manifold Gaussian Processes for regression. IJCNN Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth
2016	J	`jnl`	New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). Dagstuhl Reports Arthur Gretton, Philipp Hennig, Carl Edward Rasmussen, Bernhard Schölkopf
2016	—	`conf`	Understanding Probabilistic Sparse Gaussian Process Approximations. NIPS Matthias Bauer, Mark van der Wilk, Carl Edward Rasmussen
2015	J	`jnl`	Gaussian Processes for Data-Efficient Learning in Robotics and Control. CoRR Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen
2015	J	`jnl`	Gaussian Processes for Data-Efficient Learning in Robotics and Control. IEEE Trans. Pattern Anal. Mach. Intell. Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen
2014	J	`jnl`	Manifold Gaussian Processes for Regression. CoRR Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth
2013	J	`jnl`	Automated Bayesian System Identification with NARX Models CoRR Roger Frigola, Carl Edward Rasmussen
2013	—	`conf`	Integrated pre-processing for Bayesian nonlinear system identification with Gaussian processes. CDC Roger Frigola, Carl Edward Rasmussen
2012	A	`conf`	Gaussian Processes for time-marked time-series data. AISTATS John P. Cunningham, Zoubin Ghahramani, Carl Edward Rasmussen
2012	J	`jnl`	Model based learning of sigma points in unscented Kalman filtering. Neurocomputing Ryan D. Turner, Carl Edward Rasmussen
2012	—	`conf`	Modelling and control of nonlinear systems using Gaussian processes with partial model information. CDC Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski
2012	J	`jnl`	Robust Filtering and Smoothing with Gaussian Processes CoRR Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen
2012	J	`jnl`	Robust Filtering and Smoothing with Gaussian Processes. IEEE Trans. Autom. Control. Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen
2011	J	`jnl`	Additive Gaussian Processes CoRR David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
2011	—	`conf`	Additive Gaussian Processes. NIPS David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
2011	—	`conf`	Gaussian Process Training with Input Noise. NIPS Andrew McHutchon, Carl Edward Rasmussen
2011	—	`conf`	Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning. Robotics: Science and Systems Marc Peter Deisenroth, Carl Edward Rasmussen, Dieter Fox
2011	A*	`conf`	PILCO: A Model-Based and Data-Efficient Approach to Policy Search. ICML Marc Peter Deisenroth, Carl Edward Rasmussen
2011	—	`conf`	Reinforcement learning with reference tracking control in continuous state spaces. CDC/ECC Joseph Hall, Carl Edward Rasmussen, Jan M. Maciejowski
2010	J	`jnl`	Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution. J. Comput. Sci. Technol. Dilan Görür, Carl Edward Rasmussen
2010	—	`conf`	Gaussian Mixture Modeling with Gaussian Process Latent Variable Models. DAGM-Symposium Hannes Nickisch, Carl Edward Rasmussen
2010	A*	`conf`	Gaussian Process Change Point Models. ICML Yunus Saatci, Ryan D. Turner, Carl Edward Rasmussen
2010	J	`jnl`	Gaussian Processes for Machine Learning (GPML) Toolbox. J. Mach. Learn. Res. Carl Edward Rasmussen, Hannes Nickisch
2010	J	`jnl`	Sparse Spectrum Gaussian Process Regression. J. Mach. Learn. Res. Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal
2010	A	`conf`	State-Space Inference and Learning with Gaussian Processes. AISTATS Ryan D. Turner, Marc Peter Deisenroth, Carl Edward Rasmussen
2009	J	`jnl`	Gaussian process dynamic programming. Neurocomputing Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters
2009	J	`jnl`	Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures. IEEE ACM Trans. Comput. Biol. Bioinform. Carl Edward Rasmussen, Bernard J. de la Cruz, Zoubin Ghahramani, David L. Wild
2008	B	`conf`	Model-Based Reinforcement Learning with Continuous States and Actions. ESANN Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters
2008	—	`conf`	Probabilistic Inference for Fast Learning in Control. EWRL Carl Edward Rasmussen, Marc Peter Deisenroth
2007	J	`jnl`	The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. 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
2006	A*	`conf`	A choice model with infinitely many latent features. ICML Dilan Görür, Frank Jäkel, Carl Edward Rasmussen
2006	—	`book`	Gaussian processes for machine learning. Carl Edward Rasmussen, Christopher K. I. Williams
2005	J	`jnl`	A Unifying View of Sparse Approximate Gaussian Process Regression. J. Mach. Learn. Res. Joaquin Quiñonero Candela, Carl Edward Rasmussen
2005	J	`jnl`	Assessing Approximate Inference for Binary Gaussian Process Classification. J. Mach. Learn. Res. Malte Kuss, Carl Edward Rasmussen
2005	—	`conf`	Assessing Approximations for Gaussian Process Classification. NIPS Malte Kuss, Carl Edward Rasmussen
2005	—	`conf`	Evaluating Predictive Uncertainty Challenge. MLCW Joaquin Quiñonero Candela, Carl Edward Rasmussen, Fabian H. Sinz, Olivier Bousquet, Bernhard Schölkopf
2005	A*	`conf`	Healing the relevance vector machine through augmentation. ICML Carl Edward Rasmussen, Joaquin Quiñonero Candela
2004	Misc	`conf`	Clustering Protein Sequence and Structure Space with Infinite Gaussian Mixture Models. Pacific Symposium on Biocomputing Ananya Dubey, Seungwoo Hwang, Claudia Rangel, Carl Edward Rasmussen, Zoubin Ghahramani, David L. Wild
2004	—	`conf`	Learning Depth from Stereo. DAGM-Symposium Fabian H. Sinz, Joaquin Quiñonero Candela, Gökhan H. Bakir, Carl Edward Rasmussen, Matthias O. Franz
2004	—	`conf`	Modelling Spikes with Mixtures of Factor Analysers. DAGM-Symposium Dilan Görür, Carl Edward Rasmussen, Andreas S. Tolias, Fabian H. Sinz, Nikos K. Logothetis
2004	—	`ed.`	Pattern Recognition, 26th DAGM Symposium, August 30 - September 1, 2004, Tübingen, Germany, Proceedings DAGM-Symposium Carl Edward Rasmussen, Heinrich H. Bülthoff, Bernhard Schölkopf, Martin A. Giese
2004	—	`conf`	Semi-supervised Kernel Regression Using Whitened Function Classes. DAGM-Symposium Matthias O. Franz, Younghee Kwon, Carl Edward Rasmussen, Bernhard Schölkopf
2003	—	`conf`	Analysis of Some Methods for Reduced Rank Gaussian Process Regression. European Summer School on Multi-AgentControl Joaquin Quiñonero Candela, Carl Edward Rasmussen
2003	—	`conf`	Gaussian Processes in Machine Learning. Advanced Lectures on Machine Learning Carl Edward Rasmussen
2003	—	`conf`	Gaussian Processes in Reinforcement Learning. NIPS Carl Edward Rasmussen, Malte Kuss
2003	—	`conf`	Prediction on Spike Data Using Kernel Algorithms. NIPS Jan Eichhorn, Andreas S. Tolias, Alexander Zien, Malte Kuss, Carl Edward Rasmussen, Jason Weston, Nikos K. Logothetis, Bernhard Schölkopf
2003	—	`conf`	Propagation of uncertainty in Bayesian kernel models - application to multiple-step ahead forecasting. ICASSP (2) Joaquin Quiñonero Candela, Agathe Girard, Jan Larsen, Carl Edward Rasmussen
2003	—	`conf`	Warped Gaussian Processes. NIPS Edward Lloyd Snelson, Carl Edward Rasmussen, Zoubin Ghahramani
2002	—	`conf`	Bayesian Monte Carlo. NIPS Carl Edward Rasmussen, Zoubin Ghahramani
2002	—	`conf`	Derivative Observations in Gaussian Process Models of Dynamic Systems. NIPS E. Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl Edward Rasmussen
2002	—	`conf`	Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting. NIPS Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero Candela, Roderick Murray-Smith
2001	—	`conf`	Infinite Mixtures of Gaussian Process Experts. NIPS Carl Edward Rasmussen, Zoubin Ghahramani
2001	—	`conf`	The Infinite Hidden Markov Model. NIPS Matthew J. Beal, Zoubin Ghahramani, Carl Edward Rasmussen
2000	—	`conf`	Occam's Razor. NIPS Carl Edward Rasmussen, Zoubin Ghahramani
1999	—	`conf`	Bayesian Modelling of fMRI lime Series. NIPS Pedro A. d. F. R. Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen
1999	—	`conf`	The Infinite Gaussian Mixture Model. NIPS Carl Edward Rasmussen
1995	—	`conf`	A Practical Monte Carlo Implementation of Bayesian Learning. NIPS Carl Edward Rasmussen
1995	—	`conf`	Gaussian Processes for Regression. NIPS Christopher K. I. Williams, Carl Edward Rasmussen
1994	J	`jnl`	Pruning from Adaptive Regularization. Neural Comput. Lars Kai Hansen, Carl Edward Rasmussen
1993	J	`jnl`	Presynaptic and postsynaptic competition in models for the development of neuromuscular connections. Biol. Cybern. Carl Edward Rasmussen, David J. Willshaw