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ORCIDMarc Peter Deisenroth
Person information
- affiliation: University College London, UK
- affiliation (former): Imperial College London, Department of Computing
- affiliation (former): TU Darmstadt, Department of Computer Science
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2020 – today
- 2021
[j15]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Pathwise Conditioning of Gaussian Processes. J. Mach. Learn. Res. 22: 105:1-105:47 (2021)- [c53]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. AISTATS 2021: 1036-1044 - [c52]Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth:
Learning Contact Dynamics using Physically Structured Neural Networks. AISTATS 2021: 2152-2160 - [c51]Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky
, Marc Peter Deisenroth, Nicolas Durrande:
Matérn Gaussian Processes on Graphs. AISTATS 2021: 2593-2601 - [c50]Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth:
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. NeurIPS 2021: 17160-17169 - [i50]Sanket Kamthe, Samuel Assefa, Marc Peter Deisenroth:
Copula Flows for Synthetic Data Generation. CoRR abs/2101.00598 (2021) - [i49]Linh Tran, Maja Pantic, Marc Peter Deisenroth:
Cauchy-Schwarz Regularized Autoencoder. CoRR abs/2101.02149 (2021) - [i48]Simon Olofsson, Eduardo S. Schultz, Adel Mhamdi, Alexander Mitsos, Marc Peter Deisenroth, Ruth Misener:
Design of Dynamic Experiments for Black-Box Model Discrimination. CoRR abs/2102.03782 (2021) - [i47]Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Peter Deisenroth:
Healing Products of Gaussian Processes. CoRR abs/2102.07106 (2021) - [i46]Samuel Cohen, K. S. Sesh Kumar, Marc Peter Deisenroth:
Sliced Multi-Marginal Optimal Transport. CoRR abs/2102.07115 (2021) - [i45]Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth:
Learning Contact Dynamics using Physically Structured Neural Networks. CoRR abs/2102.11206 (2021) - [i44]Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John:
GPflux: A Library for Deep Gaussian Processes. CoRR abs/2104.05674 (2021) - [i43]Michelangelo Conserva, Marc Peter Deisenroth, K. S. Sesh Kumar:
Submodular Kernels for Efficient Rankings. CoRR abs/2105.12356 (2021) - [i42]Janith C. Petangoda, Marc Peter Deisenroth, Nicholas A. M. Monk:
Learning to Transfer: A Foliated Theory. CoRR abs/2107.10763 (2021) - [i41]Vu Nguyen, Marc Peter Deisenroth, Michael A. Osborne:
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds. CoRR abs/2110.12087 (2021) - [i40]Michael Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth:
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels. CoRR abs/2110.14423 (2021) - 2020
- [j14]Riccardo Moriconi, Marc Peter Deisenroth, K. S. Sesh Kumar:
High-dimensional Bayesian optimization using low-dimensional feature spaces. Mach. Learn. 109(9-10): 1925-1943 (2020) - [j13]Riccardo Moriconi, K. S. Sesh Kumar, Marc Peter Deisenroth:
High-dimensional Bayesian optimization with projections using quantile Gaussian processes. Optim. Lett. 14(1): 51-64 (2020) - [c49]Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth:
Variational Integrator Networks for Physically Structured Embeddings. AISTATS 2020: 3078-3087 - [c48]Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Peter Deisenroth:
Healing Products of Gaussian Process Experts. ICML 2020: 2068-2077 - [c47]Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni:
Stochastic Differential Equations with Variational Wishart Diffusions. ICML 2020: 4974-4983 - [c46]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Efficiently sampling functions from Gaussian process posteriors. ICML 2020: 10292-10302 - [c45]Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Matérn Gaussian Processes on Riemannian Manifolds. NeurIPS 2020 - [c44]Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth:
Probabilistic Active Meta-Learning. NeurIPS 2020 - [i39]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Efficiently sampling functions from Gaussian process posteriors. CoRR abs/2002.09309 (2020) - [i38]Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Matern Gaussian processes on Riemannian manifolds. CoRR abs/2006.10160 (2020) - [i37]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. CoRR abs/2006.12648 (2020) - [i36]Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni:
Stochastic Differential Equations with Variational Wishart Diffusions. CoRR abs/2006.14895 (2020) - [i35]Samuel Cohen, Michael Arbel, Marc Peter Deisenroth:
Estimating Barycenters of Measures in High Dimensions. CoRR abs/2007.07105 (2020) - [i34]Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth:
Probabilistic Active Meta-Learning. CoRR abs/2007.08949 (2020) - [i33]Janith C. Petangoda, Nick A. M. Monk, Marc Peter Deisenroth:
A Foliated View of Transfer Learning. CoRR abs/2008.00546 (2020) - [i32]Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande:
Matern Gaussian Processes on Graphs. CoRR abs/2010.15538 (2020) - [i31]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Pathwise Conditioning of Gaussian Processes. CoRR abs/2011.04026 (2020) - [i30]Daniel Lengyel, Janith C. Petangoda, Isak Falk, Kate Highnam, Michalis Lazarou, Arinbjörn Kolbeinsson, Marc Peter Deisenroth, Nicholas R. Jennings:
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability. CoRR abs/2011.07407 (2020)
2010 – 2019
- 2019
- [j12]Simon Olofsson, Lukas Hebing, Sebastian Niedenführ, Marc Peter Deisenroth, Ruth Misener:
GPdoemd: A Python package for design of experiments for model discrimination. Comput. Chem. Eng. 125: 54-70 (2019) - [j11]Simon Olofsson, Mohammad Mehrian, Roberto Calandra, Liesbet Geris, Marc Peter Deisenroth, Ruth Misener:
Bayesian Multiobjective Optimisation With Mixed Analytical and Black-Box Functions: Application to Tissue Engineering. IEEE Trans. Biomed. Eng. 66(3): 727-739 (2019) - [c43]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. ICML 2019: 5589-5598 - [i29]Riccardo Moriconi, K. S. Sesh Kumar, Marc Peter Deisenroth:
High-Dimensional Bayesian Optimization with Manifold Gaussian Processes. CoRR abs/1902.10675 (2019) - [i28]K. S. Sesh Kumar, Marc Peter Deisenroth:
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms. CoRR abs/1905.04873 (2019) - [i27]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. CoRR abs/1905.05435 (2019) - [i26]Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth:
Variational Integrator Networks for Physically Meaningful Embeddings. CoRR abs/1910.09349 (2019) - 2018
- [c42]Sanket Kamthe, Marc Peter Deisenroth:
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. AISTATS 2018: 1701-1710 - [c41]Simon Olofsson, Marc Peter Deisenroth, Ruth Misener:
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches. ICML 2018: 3905-3914 - [c40]Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth:
Gaussian Process Conditional Density Estimation. NeurIPS 2018: 2391-2401 - [c39]Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth:
Orthogonally Decoupled Variational Gaussian Processes. NeurIPS 2018: 8725-8734 - [c38]James T. Wilson, Frank Hutter, Marc Peter Deisenroth:
Maximizing acquisition functions for Bayesian optimization. NeurIPS 2018: 9906-9917 - [c37]Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth:
Meta Reinforcement Learning with Latent Variable Gaussian Processes. UAI 2018: 642-652 - [i25]Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth:
Meta Reinforcement Learning with Latent Variable Gaussian Processes. CoRR abs/1803.07551 (2018) - [i24]James T. Wilson, Frank Hutter, Marc Peter Deisenroth:
Maximizing acquisition functions for Bayesian optimization. CoRR abs/1805.10196 (2018) - [i23]Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth:
Orthogonally Decoupled Variational Gaussian Processes. CoRR abs/1809.08820 (2018) - [i22]Vincent Dutordoir, Hugh Salimbeni, Marc Peter Deisenroth, James Hensman:
Gaussian Process Conditional Density Estimation. CoRR abs/1810.12750 (2018) - 2017
- [j10]Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Ai Poh Loh, Prahlad Vadakkepat, Gerhard Neumann:
Model-based contextual policy search for data-efficient generalization of robot skills. Artif. Intell. 247: 415-439 (2017) - [j9]Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath:
Deep Reinforcement Learning: A Brief Survey. IEEE Signal Process. Mag. 34(6): 26-38 (2017) - [j8]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Gaussian Process Domain Experts for Modeling of Facial Affect. IEEE Trans. Image Process. 26(10): 4697-4711 (2017) - [c36]Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth:
Customer Lifetime Value Prediction Using Embeddings. KDD 2017: 1753-1762 - [c35]Hugh Salimbeni, Marc Peter Deisenroth:
Doubly Stochastic Variational Inference for Deep Gaussian Processes. NIPS 2017: 4588-4599 - [c34]Stefanos Eleftheriadis, Tom Nicholson, Marc Peter Deisenroth, James Hensman:
Identification of Gaussian Process State Space Models. NIPS 2017: 5309-5319 - [c33]Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth:
Probabilistic Inference of Twitter Users' Age Based on What They Follow. ECML/PKDD (3) 2017: 191-203 - [i21]Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth:
Customer Life Time Value Prediction Using Embeddings. CoRR abs/1703.02596 (2017) - [i20]Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth:
Neural Embeddings of Graphs in Hyperbolic Space. CoRR abs/1705.10359 (2017) - [i19]Sanket Kamthe, Marc Peter Deisenroth:
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. CoRR abs/1706.06491 (2017) - [i18]Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath:
A Brief Survey of Deep Reinforcement Learning. CoRR abs/1708.05866 (2017) - [i17]James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc Peter Deisenroth:
The reparameterization trick for acquisition functions. CoRR abs/1712.00424 (2017) - 2016
- [j7]Roberto Calandra, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
Bayesian optimization for learning gaits under uncertainty - An experimental comparison on a dynamic bipedal walker. Ann. Math. Artif. Intell. 76(1-2): 5-23 (2016) - [c32]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units. ACCV (2) 2016: 154-170 - [c31]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis. CVPR Workshops 2016: 1469-1477 - [c30]Maciej Kurek, Marc Peter Deisenroth, Wayne Luk, Timothy John Todman:
Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs. FCCM 2016: 84-87 - [c29]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for regression. IJCNN 2016: 3338-3345 - [c28]Maja Pantic, Vanessa Evers, Marc Peter Deisenroth, Luis Merino, Björn W. Schuller:
Social and Affective Robotics Tutorial. ACM Multimedia 2016: 1477-1478 - [i16]Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth:
Real-Time Association Mining in Large Social Networks. CoRR abs/1601.03958 (2016) - [i15]Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth:
Detecting the Age of Twitter Users. CoRR abs/1601.04621 (2016) - [i14]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis. CoRR abs/1604.02917 (2016) - [i13]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units. CoRR abs/1608.04664 (2016) - 2015
- [j6]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) - [c27]Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Jan Peters:
Learning torque control in presence of contacts using tactile sensing from robot skin. Humanoids 2015: 690-695 - [c26]Marc Peter Deisenroth, Jun Wei Ng:
Distributed Gaussian Processes. ICML 2015: 1481-1490 - [c25]Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Elmar Rueckert, Jan Peters:
Learning inverse dynamics models with contacts. ICRA 2015: 3186-3191 - [i12]Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
From Pixels to Torques: Policy Learning with Deep Dynamical Models. CoRR abs/1502.02251 (2015) - [i11]Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen:
Gaussian Processes for Data-Efficient Learning in Robotics and Control. CoRR abs/1502.02860 (2015) - [i10]John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. CoRR abs/1510.02173 (2015) - [i9]Doniyor Ulmasov, Caroline Baroukh, Benoît Chachuat, Marc Peter Deisenroth, Ruth Misener:
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems. CoRR abs/1511.05385 (2015) - 2014
- [c24]Nooshin HajiGhassemi, Marc Peter Deisenroth:
Analytic Long-Term Forecasting with Periodic Gaussian Processes. AISTATS 2014: 303-311 - [c23]Sanket Kamthe, Jan Peters, Marc Peter Deisenroth:
Multi-modal filtering for non-linear estimation. ICASSP 2014: 7979-7983 - [c22]Roberto Calandra, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
An experimental comparison of Bayesian optimization for bipedal locomotion. ICRA 2014: 1951-1958 - [c21]Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox:
Multi-task policy search for robotics. ICRA 2014: 3876-3881 - [c20]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 - [c19]Roberto Calandra, Nakul Gopalan, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
Bayesian Gait Optimization for Bipedal Locomotion. LION 2014: 274-290 - [i8]Sanket Kamthe, Jan Peters, Marc Peter Deisenroth:
Multi-modal filtering for non-linear estimation. CoRR abs/1401.0077 (2014) - [i7]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for Regression. CoRR abs/1402.5876 (2014) - [i6]Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
Learning deep dynamical models from image pixels. CoRR abs/1410.7550 (2014) - [i5]Jun Wei Ng, Marc Peter Deisenroth:
Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression. CoRR abs/1412.3078 (2014) - 2013
- [j5]Peter Englert, Alexandros Paraschos, Marc Peter Deisenroth, Jan Peters:
Probabilistic model-based imitation learning. Adapt. Behav. 21(5): 388-403 (2013) - [j4]Marc Peter Deisenroth, Gerhard Neumann, Jan Peters:
A Survey on Policy Search for Robotics. Found. Trends Robotics 2(1-2): 1-142 (2013) - [j3]Zhikun Wang, Katharina Mülling, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic movement modeling for intention inference in human-robot interaction. Int. J. Robotics Res. 32(7): 841-858 (2013) - [c18]Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Gerhard Neumann:
Data-Efficient Generalization of Robot Skills with Contextual Policy Search. AAAI 2013 - [c17]Nakul Gopalan, Marc Peter Deisenroth, Jan Peters:
Feedback error learning for rhythmic motor primitives. ICRA 2013: 1317-1322 - [c16]Peter Englert, Alexandros Paraschos, Jan Peters, Marc Peter Deisenroth:
Model-based imitation learning by probabilistic trajectory matching. ICRA 2013: 1922-1927 - [i4]Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox:
Multi-Task Policy Search. CoRR abs/1307.0813 (2013) - 2012
- [j2]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) - [c15]Marc Peter Deisenroth, Csaba Szepesvári, Jan Peters:
Preface. EWRL 2012 - [c14]Roberto Calandra, Tapani Raiko, Marc Peter Deisenroth, Federico Montesino-Pouzols:
Learning Deep Belief Networks from Non-stationary Streams. ICANN (2) 2012: 379-386 - [c13]Marc Peter Deisenroth, Roberto Calandra, André Seyfarth, Jan Peters:
Toward fast policy search for learning legged locomotion. IROS 2012: 1787-1792 - [c12]Marc Peter Deisenroth, Shakir Mohamed:
Expectation Propagation in Gaussian Process Dynamical Systems. NIPS 2012: 2618-2626 - [c11]Zhikun Wang, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic Modeling of Human Movements for Intention Inference. Robotics: Science and Systems 2012 - [e1]Marc Peter Deisenroth, Csaba Szepesvári, Jan Peters:
Proceedings of the Tenth European Workshop on Reinforcement Learning, EWRL 2012, Edinburgh, Scotland, UK, June, 2012. JMLR Proceedings 24, JMLR.org 2012 [contents] - [i3]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) - [i2]Marc Peter Deisenroth, Shakir Mohamed:
Expectation Propagation in Gaussian Process Dynamical Systems. CoRR abs/1207.2940 (2012) - 2011
- [c10]Marc Peter Deisenroth, Henrik Ohlsson:
A general perspective on Gaussian filtering and smoothing: Explaining current and deriving new algorithms. ACC 2011: 1807-1812 - [c9]Marc Peter Deisenroth, Carl Edward Rasmussen:
PILCO: A Model-Based and Data-Efficient Approach to Policy Search. ICML 2011: 465-472 - [c8]Cynthia Matuszek, Brian Mayton, Roberto Aimi, Marc Peter Deisenroth, Liefeng Bo, Robert Chu, Mike Kung, Louis LeGrand, Joshua R. Smith, Dieter Fox:
Gambit: An autonomous chess-playing robotic system. ICRA 2011: 4291-4297 - [c7]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 - 2010
- [b1]Marc Peter Deisenroth:
Efficient reinforcement learning using Gaussian processes. Karlsruhe Institute of Technology, 2010, ISBN 978-3-86644-569-7, pp. 1-205 - [c6]Ryan D. Turner, Marc Peter Deisenroth, Carl Edward Rasmussen:
State-Space Inference and Learning with Gaussian Processes. AISTATS 2010: 868-875 - [i1]Marc Peter Deisenroth, Henrik Ohlsson:
A Probabilistic Perspective on Gaussian Filtering and Smoothing. CoRR abs/1006.2165 (2010)
2000 – 2009
- 2009
- [j1]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Gaussian process dynamic programming. Neurocomputing 72(7-9): 1508-1524 (2009) - [c5]Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck:
Analytic moment-based Gaussian process filtering. ICML 2009: 225-232 - 2008
- [c4]Marc Peter Deisenroth, Jan Peters, Carl E. Rasmussen:
Approximate dynamic programming with Gaussian processes. ACC 2008: 4480-4485 - [c3]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Model-Based Reinforcement Learning with Continuous States and Actions. ESANN 2008: 19-24 - [c2]Carl Edward Rasmussen, Marc Peter Deisenroth:
Probabilistic Inference for Fast Learning in Control. EWRL 2008: 229-242 - 2006
- [c1]Marc Peter Deisenroth, Toshiyuki Ohtsuka, Florian Weissel, Dietrich Brunn, Uwe D. Hanebeck:
Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle. MFI 2006: 371-376