Publications

Books and Proceedings

  1. Marc P. Deisenroth, A. Aldo Faisal, Cheng Soon Ong, Mathematics for Machine Learning (in preparation, drafts online)
  2. Marc P. Deisenroth, Gerhard Neumann, Jan Peters, A Survey on Policy Search for Robotics, Foundations and Trends in Robotics, volume 2, pp.1–142, 2013. ISBN 978-160-198-702-0
  3. Marc P. Deisenroth, Csaba Szepesvári, Jan Peters, Proceedings of the 10th European Workshop on Reinforcement Learning, 2012
  4. Marc P. Deisenroth, Efficient Reinforcement Learning using Gaussian Processes, volume 9, KIT Scientific Publishing, 2010. ISBN 978-3-86644-569-7

Journal Papers

  1. Simon Olofsson, Mohammad Mehrian, Roberto Calandra, Liesbet Geris, Marc P. Deisenroth, Ruth Misener, Bayesian Multi-Objective Optimisation with Mixed Analytical and Black-Box Functions: Application to Tissue Engineering, IEEE Transactions on Biomedical Engineering, 2018
  2. Benjamin Chamberlain, Josh Levy-Kramer, Clive Humby, Marc P. Deisenroth, Real-Time Community Detection in Full Social Networks on a Laptop, PLoS ONE, volume 13, pp. e0188702, 2018
  3. Kai Arulkumaran, Marc P. Deisenroth, Miles Brundage, Anil A. Barath, Deep Reinforcement Learning: A Brief Survey, IEEE Signal Processing Magazine, volume 34, pp. 26–38, 2017
  4. Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic, Gaussian Process Domain Experts for Modeling of Facial Affect, IEEE Transactions on Image Processing, volume 26, pp. 4697–4711, 2017
  5. Andras Kupcsik, Marc P. Deisenroth, Jan Peters, Loh Ai Poha, Prahlad Vadakkepata, Gerhard Neumann, Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills, Artificial Intelligence, volume 247, pp. 415–439, 2017
  6. Roberto Calandra, Andre Seyfarth, Jan Peters, Marc P. Deisenroth, Bayesian Optimization for Learning Gaits under Uncertainty, Annals of Mathematics and Artificial Intelligence, volume 76, pp. 5–23, 2016
  7. Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen, Gaussian Processes for Data-Efficient Learning in Robotics and Control, IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 37, pp. 408–423, 2015
  8. Zhikun Wang, Katharina Mülling, Marc P. Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters, Probabilistic Movement Modeling for Intention Inference in Human-Robot Interaction, International Journal of Robotics Research, volume 32, pp. 841–858, 2013
  9. Peter Englert, Alexandros Paraschos, Jan Peters, Marc P. Deisenroth, Probabilistic Model-based Imitation Learning, Adaptive Behavior, volume 21, pp. 388–403, 2013
  10. Marc P. Deisenroth, Ryan Turner, Marco Huber, Uwe D. Hanebeck, Carl E. Rasmussen, Robust Filtering and Smoothing with Gaussian Processes, IEEE Transactions on Automatic Control, volume 57, pp.1865–1871, 2012
  11. Marc P. Deisenroth, Carl E. Rasmussen, Jan Peters, Gaussian Process Dynamic Programming, Neurocomputing, volume 72, pp. 1508–1524, 2009

Conference Papers

  1. James T. Wilson, Frank Hutter, Marc P. Deisenroth, Maximizing Acquisition Functions for Bayesian Optimization, Advances in Neural Information Processing Systems (NIPS), 2018
  2. Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc P. Deisenroth, Orthogonally Decoupled Variational Gaussian Processes, Advances in Neural Information Processing Systems (NIPS), 2018
  3. Vincent Dutordoir, Hugh Salimbeni, Marc P. Deisenroth, James Hensman, Gaussian Process Conditional Density Estimation, Advances in Neural Information Processing Systems (NIPS), 2018
  4. Steindór Sæmundsson, Katja Hofmann, Marc P. Deisenroth, Meta Reinforcement Learning with Latent Variable Gaussian Processes, Proceedings of the International the Conference on Uncertainty in Artificial Intelligence (UAI), 2018
  5. Simon Olofsson, Marc P. Deisenroth, Ruth Misener, Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches, Proceedings of the International the International Conference on Machine Learning (ICML), 2018
  6. Sanket Kamthe, Marc P. Deisenroth, Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control, Proceedings of the International the Conference on Artificial Intelligence and Statistics (AISTATS), 2018
  7. Hugh Salimbeni, Marc P. Deisenroth, Doubly Stochastic Variational Inference for Deep Gaussian Processes, Advances in Neural Information Processing Systems (NIPS), 2017
  8. Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc P. Deisenroth, James Hensman, Identification of Gaussian Process State Space Models, Advances in Neural Information Processing Systems (NIPS), 2017
  9. Benjamin P. Chamberlain, Angelo Cardoso, C.H. Bryan Liu, Roberto Pagliari, Marc P. Deisenroth, Customer Life Time Value Prediction Using Embeddings, Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), 2017
  10. Benjamin Paul Chamberlain, Clive Humby, Marc P. Deisenroth, Probabilistic Inference of Twitter Users' Age based on What They Follow, Proceedings of the European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2017
  11. Simon Olofsson, Mohammad Mehrian, Liesbet Geris, Roberto Calandra, Marc P. Deisenroth, Ruth Misener, Bayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-up, Proceedings of the European Symposium on Computer Aided Process Engineering (ESCAPE), 2017
  12. Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic, Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units, Proceedings of the Asian Conference on Computer Vision (ACCV), 2016
  13. Kshitij Tiwari, Valentin Honore, Sungmoon Jeong, Nak Young Chong, Marc P. Deisenroth, Resource-Constrained Decentralized Active Sensing using Distributed Gaussian Processes for Multi-Robots, Proceedings of the International Conference on Control, Automation and Systems (ICCAS), 2016. Best Student Paper Award at ICCAS
  14. Doniyor Ulmasov, Caroline Baroukh, Benoit Chachuat, Marc P. Deisenroth, Ruth Misener, Bayesian Optimization with Dimension Scheduling: Application to Biological Systems, Proceedings of the European Symposium on Computer Aided Process Engineering, 2016
  15. Roberto Calandra, Jan Peters, Carl E. Rasmussen, Marc P. Deisenroth, Manifold Gaussian Processes for Regression, Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), 2016
  16. Maciej Kurek, Marc P. Deisenroth, Wayne Luk, Timothy Todman, Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs, Proceedings of the IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2016
  17. Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth, Learning Deep Dynamical Models From Image Pixels, IFAC Symposium on System Identification (SYSID), 2015
  18. Roberto Calandra, Serena Ivaldi, Marc P. Deisenroth, Jan Peters, Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin, Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS), 2015
  19. Marc P. Deisenroth, Jun W. Ng, Distributed Gaussian Processes, Proceedings of the International Conference on Machine Learning (ICML), 2015
  20. Roberto Calandra, Serena Ivaldi, Marc P. Deisenroth, Elmar Rueckert, Jan Peters, Learning Inverse Dynamics Models with Contacts, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2015
  21. Sanket Kamthe, Jan Peters, Marc P. Deisenroth, Multi-Modal Filtering for Non-linear Estimation, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2014
  22. Nooshin HajiGhassemi, Marc P. Deisenroth, Analytic Long-Term Forecasting with Periodic Gaussian Processes, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2014
  23. Roberto Calandra, Jan Peters, Andre Seyfarth, Marc P. Deisenroth, An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2014
  24. Marc P. Deisenroth, Peter Englert, Jan Peters, Dieter Fox, Multi-Task Policy Search for Robotics, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2014, Best Cognitive Robotics Paper Award at ICRA 2014
  25. Roberto Calandra, Nakul Gopalan, Andre Seyfarth, Jan Peters, Marc P. Deisenroth, Bayesian Gait Optimization for Bipedal Locomotion, volume 8426 (Lecture Notes in Computer Science), pp.274–290, 2014
  26. Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois Knoll, Jan Peters, Marc P. Deisenroth, Policy Search For Learning Robot Control Using Sparse Data, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2014
  27. Andras Kupcsik, Marc P. Deisenroth, Jan Peters, Gerhard Neumann, Data-Efficient Generalization of Robot Skills with Contextual Policy Search, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2013
  28. Peter Englert, Alexandros Paraschos, Jan Peters, Marc P. Deisenroth, Model-based Imitation Learning by Probabilistic Trajectory Matching, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2013
  29. Nakul Gopalan, Marc P. Deisenroth, Jan Peters, Feedback Error Learning for Rhythmic Motor Primitives, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2013
  30. Marc P. Deisenroth, Shakir Mohamed, Expectation Propagation in Gaussian Process Dynamical Systems, Advances in Neural Information Processing Systems (NIPS), 2012
  31. Marc P. Deisenroth, Jan Peters, Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise, European Workshop on Reinforcement Learning (EWRL), 2012
  32. Marc P. Deisenroth, Roberto Calandra, Andr'e Seyfarth, Jan Peters, Toward Fast Policy Search for Learning Legged Locomotion, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2012
  33. Roberto Calandra, Tapani Raiko, Marc P. Deisenroth, Federico Montesino Pouzols, Learning Deep Belief Networks from Non-Stationary Streams, Proceedings of International Conference on Artificial Neural Networks (ICANN), 2012
  34. Zhikun Wang, Marc P. Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters, Probabilistic Modeling of Human Dynamics for Intention Inference, Proceedings of Robotics: Science & Systems (RSS), 2012
  35. Marc P. Deisenroth, Carl E. Rasmussen, Dieter Fox, Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning, Proceedings of the International Conference on Robotics: Science and Systems (RSS), 2011
  36. Marc P. Deisenroth, Carl E. Rasmussen, PILCO: A Model-Based and Data-Efficient Approach to Policy Search, Proceedings of the International Conference on Machine Learning (ICML), 2011
  37. Marc P. Deisenroth, Henrik Ohlsson, A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving New Algorithms, Proceedings of the American Control Conference (ACC), 2011
  38. Cynthia Matuszek, Brian Mayton, Roberto Aimi, Marc P. Deisenroth, Liefeng Bo, Robert Chu, Michael Kung, Louis LeGrand, Joshua R. Smith, Dieter Fox, Gambit: An Autonomous Chess-Playing Robotic System, Proceedings of the International Conference on Robotics and Automation (ICRA), 2011
  39. Ryan Turner, Marc P. Deisenroth, Carl E. Rasmussen, State-Space Inference and Learning with Gaussian Processes, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS), pp.868–875, 2010
  40. Marc P. Deisenroth, Carl E. Rasmussen, Efficient Reinforcement Learning for Motor Control, Proceedings of the 10th International Workshop on Systems and Control, 2009
  41. Marc P. Deisenroth, Marco F. Huber, Uwe D. Hanebeck, Analytic Moment-based Gaussian Process Filtering, Proceedings of the 26th International Conference on Machine Learning (ICML), pp.225–232, 2009
  42. Carl E. Rasmussen, Marc P. Deisenroth, Probabilistic Inference for Fast Learning in Control, Proceedings of the 8th European Workshop on Reinforcement Learning (EWRL), volume 5323, pp.229–242, 2008
  43. Marc P. Deisenroth, Jan Peters, Carl E. Rasmussen, Approximate Dynamic Programming with Gaussian Processes, Proceedings of the 2008 American Control Conference (ACC), pp.4480–4485, 2008
  44. Marc P. Deisenroth, Carl E. Rasmussen, Jan Peters, Model-Based Reinforcement Learning with Continuous States and Actions, Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN), pp. 19–24, 2008
  45. Marc P. Deisenroth, Florian Weissel, Toshiyuki Ohtsuka, Uwe D. Hanebeck, Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces, Proceedings of the 9th European Control Conference 2007 (ECC), pp. 3664–3671, 2007
  46. Marc P. 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, Proceedings of the 6th IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 371–376, 2006

Pre-Prints, Workshop Papers, Technical Reports

  1. James T. Wilson, Frank Hutter, Marc P. Deisenroth, Maximizing Acquisition Functions for Bayesian Optimization, arXiv:1805.10196, 2018
  2. Hugh Salimbeni, Marc P. Deisenroth, Deeply Non-Stationary Gaussian Processes, NIPS Workshop on Bayesian Deep Learning, 2017
  3. James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc P. Deisenroth, The Reparameterization Trick for Acquisition Functions, NIPS Workshop on Bayesian Optimization, 2017
  4. Benjamin P. Chamberlain, James Clough, Marc P. Deisenroth, Neural Embeddings of Graphs in Hyperbolic Space, International Workshop on Mining and Learning with Graphs, 2017
  5. Hugh Salimbeni, Marc P. Deisenroth, Gaussian Process Multiclass Classification with Dirichlet Priors for Imbalanced Data, NIPS Workshop on Practical Bayesian Nonparametrics, 2016
  6. Matthew C. H. Lee, Hugh Salimbeni, Marc P. Deisenroth, Ben Glocker, Patch Kernels for Gaussian Processes in High-Dimensional Imaging Problems, NIPS Workshop on Practical Bayesian Nonparametrics, 2016
  7. Gianfranco Bertone, Marc P. Deisenroth, Jong Soo Kim, Sebastian Liem, Roberto Ruiz de Austri, Max Welling, Accelerating the BSM Interpretation of LHC Data with Machine Learning, arXiv:1611.02704, 2016
  8. Ben Chamberlain, Clive Humby, Marc P. Deisenroth, Real-Time Community Detection in Large Social Networks on a Laptop, International Workshop on Mining and Learning with Graphs, 2016
  9. Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic, Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis, arXiv:1604.02917, 2016
  10. Benjamin P. Chamberlain, Clive Humby, Marc P. Deisenroth, Detecting the Age of Twitter Users, arXiv:1601.04621, 2016
  11. Benjamin P. Chamberlain, Josh Levy-Kramer, Clive Humby, Marc P. Deisenroth, Real-Time Association Mining in Large Social Networks, arXiv:1601.03958, 2016
  12. John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth, Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models, arXiv:1510.02173, 2015
  13. Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth, From Pixels to Torques: Policy Learning using Deep Dynamical Models, arXiv:1502.02251, 2015
  14. Roberto Calandra, Jan Peters, Marc P. Deisenroth, Pareto Front Modeling for Sensitivity Analysis in Multi-Objective Bayesian Optimization, Workshop on Bayesian Optimization in Academia and Industry at NIPS, 2014
  15. Jun Wei Ng, Marc P. Deisenroth, Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression, arXiv:1412.3078, 2014
  16. Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth, Learning Deep Dynamical Models From Image Pixels, arXiv:1410.7550, 2014
  17. Marc P. Deisenroth, Peter Engert, Jan Peters, Dieter Fox, Multi-Task Policy Search, arXiv:1307.0813, 2013

PhD Thesis