Publications

Sicelukwanda Zwane, Denis Hadjivelichkov, Yicheng Luo, Yasemin Bekiroglu, Dimitrios Kanoulas, Marc P. Deisenroth (2023). Safe Trajectory Sampling in Model-based Reinforcement Learning. Proceedings of the International Conference on Automation Science and Engineering (CASE).

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Suman Ravuri, Melanie Rey, Shakir Mohamed, Marc P. Deisenroth (2023). Understanding Deep Generative Models with Generalized Empirical Likelihoods. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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Organizers of Queer in AI, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubička, Hang Yuan, Hetvi J, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Melvin Selim Atay, Milind Agarwal, Nyx McLean, Pan Xu, A Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, ST John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dǒng, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark (2023). Queer In AI: A Case Study in Community-Led Participatory AI. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT).

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Yiting Chen, Junnan Jiang, Ruiqi Lei, Yasemin Bekiroglu, Fei Chen, Miao Li (2023). Deep Grasp Adaptation through Domain Transfer. IEEE International Conference on Robotics and Automation (ICRA).

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Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc P. Deisenroth (2023). Optimal Transport for Offline Imitation Learning. Proceedings of the International Conference on Learning Representations (ICLR).

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Jake Cunningham, Daniel de Souza, So Takao, Marc van der Wilk, Marc P. Deisenroth (2023). Actually Sparse Variational Gaussian Processes. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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So Takao, Sean Nassimiha, Peter Dudfield, Jack Kelly, Marc P. Deisenroth (2022). Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes. NeurIPS Workshop on Tackling Climate Change with Machine Learning.

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Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc P. Deisenroth (2022). Optimal Transport for Offline Imitation Learning. NeurIPS Workshop on Offline Reinforcement Learning.

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Jake Cunningham, So Takao, Mark van der Wilk, Marc P. Deisenroth (2022). Actually Sparse Variational Gaussian Processes. NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems.

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Denis Hadjivelichkov, Sicelukwanda Zwane, Lourdes Agapito, Marc P. Deisenroth, Dimitrios Kanoulas (2022). One-Shot Transfer of Affordance Regions? AffCorrs!. Proceedings of the Conference on Robot Learning (CoRL).

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Zuka Murvanidze, Marc P. Deisenroth, Yasemin Bekiroglu (2022). Enhanced GPIS Learning Based on Local and Global Focus Areas. IEEE Robotics and Automation Letters.

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Michelangelo Conserva, Marc P. Deisenroth, K. S. Sesh Kumar (2022). The Graph Cut Kernel for Ranked Data. Transactions on Machine Learning Research.

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Sanket Kamthe, So Takao, Shakir Mohamed, Marc P. Deisenroth (2022). Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation. Transactions on Machine Learning Research.

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Linh Tran, Maja Pantic, Marc P. Deisenroth (2022). Cauchy-Schwarz Regularized Autoencoder. Journal of Machine Learning Research.

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Daniel de Souza, Diego Mesquita, Samuel Kaski, Luigi Acerbi (2022). Parallel MCMC Without Embarrassing Failures. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Hadi Hajieghrary, Marc P. Deisenroth, Yasemin Bekiroglu (2022). Bayesian Optimization based Nonlinear Adaptive PID Design for Robust Control of the Joints at Mobile Manipulators. Proceedings of the IEEE International Conference on Automation Science and Engineering.

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Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc P. Deisenroth (2021). Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. Advances in Neural Information Processing Systems (NeurIPS).

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Noémie Jaquier, Viacheslav Borovitskiy, Andrei Smolensky, Alexander Terenin, Tamim Asfour, Leonel Rozo (2021). Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels. Proceedings of the Conference on Robot Learning.

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Florin Gogianu, Tudor Berariu, Mihaela Rosca, Claudia Clopath, Lucian Busoniu, Razvan Pascanu (2021). Spectral Normalization for Deep Reinforcement Learning: an Optimisation Perspective. Proceedings of the International Conference on Machine Learning (ICML).

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Samuel Cohen, Brandon Amos, Yaron Lipman (2021). Riemannian Convex Potential Flows. Proceedings of the International Conference on Machine Learning (ICML).

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Mihaela Rosca, Yan Wu, Benoit Dherin, David Barrett (2021). Discretization Drift in Two-Player Games. Proceedings of the International Conference on Machine Learning (ICML).

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James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc P. Deisenroth (2021). Pathwise Conditioning of Gaussian Processes. Journal of Machine Learning Research.

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Nenad Tomasev, Kevin R. McKee, Jackie Kay, Shakir Mohamed (2021). Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.

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Jacob Menick, Erich Elsen, Utku Evci, Simon Osindero, Karen Simonyan, Alex Graves (2021). A Practical Sparse Approximation for Real Time Recurrent Learning. International Conference on Learning Representations (ICLR).

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Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Deisenroth, Nicolas Durrande (2021). Matérn Gaussian Processes on Graphs. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Deisenroth (2021). Learning Contact Dynamics using Physically Structured Neural Networks. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Deisenroth (2021). Aligning Time Series on Incomparable Spaces. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc P. Deisenroth, ST John (2021). GPflux: A Library for Deep Gaussian Processes.

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Christiana de Farias, Naresh Marturi, Rustam Stolkin, Yasemin Bekiroglu (2021). Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects. IEEE Robotics and Automation Letters.

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Yicheng Luo, Antonio Filieri, Yuan Zhou (2021). Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis. Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ‘21).

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Jean Kaddour, Steindór Sæmundsson, Marc Deisenroth (2020). Probabilistic Active Meta-Learning. Advances in Neural Information Processing Systems (NeurIPS).

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Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth (2020). Matérn Gaussian Processes on Riemannian Manifolds. Advances in Neural Information Processing Systems (NeurIPS).

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Alexander Terenin, Måns Magnusson, Leif Jonsson (2020). Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).

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Riccardo Moriconi, Marc Deisenroth, K. S. Sesh Kumar (2020). High-Dimensional Bayesian Optimization with Manifold Gaussian Processes. Machine Learning.

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Janith Petangoda, Nick A. M. Monk, Marc Deisenroth (2020). A Foliated View of Transfer Learning. arXiv:2008.00546.

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Samuel Cohen, Michael Arbel, Marc Deisenroth (2020). Estimating Barycenters of Measures in High Dimensions. arXiv:2007.07105.

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Martin Jørgensen, Marc Deisenroth, Hugh Salimbeni (2020). Stochastic Differential Equations with Variational Wishart Diffusions. Proceedings of the International Conference on Machine Learning (ICML).

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Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Deisenroth (2020). Healing Products of Gaussian Process Experts. Proceedings of the International Conference on Machine Learning (ICML).

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James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Deisenroth (2020). Efficiently Sampling Functions from Gaussian Process Posteriors. Proceedings of the International Conference on Machine Learning (ICML).

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Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Deisenroth (2020). Aligning Time Series on Incomparable Spaces. arXiv:2006.12648.

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Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Deisenroth (2020). Variational Integrator Networks for Physically Structured Embeddings. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Alexander Terenin, Daniel Simpson, David Draper (2020). Asynchronous Gibbs Sampling. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Marc Deisenroth, A. Aldo Faisal, Cheng Soon Ong (2020). Mathematics for Machine Learning. Cambridge University Press.

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Riccardo Moriconi, K. S. Sesh Kumar, Marc Deisenroth (2020). High-Dimensional Bayesian Optimization with Projections using Quantile Gaussian Processes. Optimization Letters.

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Simon Olofsson (2020). Gaussian Processes for Hybridisation of Analytical and Data-Driven Approaches for Design of Experiments.

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Samuel Cohen, Dino Sejdinovic (2019). On the Gromov-Wasserstein distance and Coupled Deep Generative Models. OTML Workshop at NeurIPS.

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Riccardo Moriconi, Marc P. Deisenroth, K. S. Sesh Kumar (2019). High-Dimensional Bayesian Optimization Using Low-Dimensional Feature Spaces. Bayesian Deep Learning Workshop at NeurIPS.

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Janith C. Petangoda, Sergio Pascoal-Diaz, Vincent Adam, Peter Vrancx, Jordi Grau-Moya (2019). Disentangled Skill Embeddings for Reinforcement Learning. NeurIPS Workshop on Learning Transferable Skills.

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Steindór Sæmundsson, Katja Hofmann, Marc Deisenroth (2019). Variational Integrator Networks. Bayesian Deep Learning Workshop at NeurIPS.

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K. S. Sesh Kumar, F. Bach, T. Pock (2019). Fast Decomposable Submodular Function Minimization using Constrained Total Variation. Advances in Neural Information Processing Systems.

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Hugh Salimbeni (2019). Deep Gaussian Processes: Advances in Models and Inference.

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K. S. Sesh Kumar, Marc P. Deisenroth (2019). Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms. arXiv:1905.04873.

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Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc P. Deisenroth (2019). Deep Gaussian Processes with Importance-Weighted Variational Inference. Proceedings of the International Conference on Machine Learning (ICML).

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Gianfranco Bertone, Marc P. Deisenroth, Jong S. Kim, Sebastian Liem, Roberto Ruiz de Austri, Max Welling (2019). Accelerating the BSM Interpretation of LHC Data with Machine Learning. Physics of the Dark Universe.

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Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc P. Deisenroth (2019). Variational Integrator Networks for Physically Meaningful Embeddings. arXiv:1910.09349.

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Simon Olofsson, Lukas Hebing, Sebastian Niedenfuehr, Marc Deisenroth, Ruth Misener (2019). GPdoemd: A Python Package for Design of Experiments for Model Discrimination. Computers and Chemical Engineering.

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Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc P. Deisenroth (2018). Orthogonally Decoupled Variational Gaussian Processes. Advances in Neural Information Processing Systems (NeurIPS).

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James Wilson, Frank Hutter, Marc P. Deisenroth (2018). Maximizing Acquisition Functions for Bayesian Optimization. Advances in Neural Information Processing Systems (NeurIPS).

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Benjamin Paul Chamberlain (2018). Practical Challenges of Learning andRepresentation for Large Graphs.

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Steindór Sæmundsson, Katja Hofmann, Marc Deisenroth (2018). Meta Reinforcement Learning with Latent Variable Gaussian Processes. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

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Sanket Kamthe, Marc P. Deisenroth (2018). Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Benjamin P. Chamberlain, Josh Levy-Kramer, Clive Humby, Marc P. Deisenroth (2018). Real-Time Community Detection in Full Social Networks on a Laptop. PLOS ONE.

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Vincent Dutordoir, Hugh Salimbeni, Marc P. Deisenroth, James Hensman (2018). Gaussian Process Conditional Density Estimation. Advances in Neural Information Processing Systems (NeurIPS).

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Simon Olofsson, Marc P. Deisenroth, Ruth Misener (2018). Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches. Proceedings of the International Conference on Machine Learning.

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Simon Olofsson, Mohammad Mehrian, Roberto Calandra, Liesbet Geris, Marc P. Deisenroth, Ruth Misener (2018). Bayesian Multi-Objective Optimisation with Mixed Analytical and Black-Box Functions: Application to Tissue Engineering. IEEE Transactions on Biomedical Engineering.

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Hugh Salimbeni, Marc P. Deisenroth (2017). Doubly Stochastic Variational Inference for Deep Gaussian Processes. Advances in Neural Information Processing Systems (NIPS).

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James Wilson, Riccardo Moriconi, Frank Hutter, Marc P. Deisenroth (2017). The Reparameterization Trick for Acquisition Functions. NIPS Workshop on Bayesian Optimization.

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Benjamin P. Chamberlain, Clive Humby, Marc P. Deisenroth (2017). 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).

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Benjamin P. Chamberlain, James Clough, Marc P. Deisenroth (2017). Neural Embeddings of Graphs in Hyperbolic Space. International Workshop on Mining and Learning with Graphs.

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Andras Kupcsik, Marc P. Deisenroth, Jan Peters, Loh Ai Poha, Prahlad Vadakkepata, Gerhard Neumann (2017). Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills. Artificial Intelligence.

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Stefanos Eleftheriadis, Thomas F. W. Nicholson, Marc P. Deisenroth, James Hensman (2017). Identification of Gaussian Process State Space Models. Advances in Neural Information Processing Systems (NIPS).

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Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic (2017). Gaussian Process Domain Experts for Modeling of Facial Affect. IEEE Transactions on Image Processing.

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Hugh Salimbeni, Marc P. Deisenroth (2017). Deeply Non-Stationary Gaussian Processes. NIPS Workshop on Bayesian Deep Learning.

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Benjamin P. Chamberlain, Angelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc P. Deisenroth (2017). Customer Life Time Value Prediction Using Embeddings. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD).

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Simon Olofsson, Mohammad Mehrian, Liesbet Geris, Roberto Calandra, Marc P. Deisenroth, Ruth Misener (2017). Bayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-up. Proceedings of the European Symposium on Computer Aided Process Engineering (ESCAPE).

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Kai Arulkumaran, Marc P. Deisenroth, Miles Brundage, Anil A. Barath (2017). A Brief Survey of Deep Reinforcement Learning. IEEE Signal Processing Magazine.

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Kshitij Tiwari, Valentin Honore, Sungmoon Jeong, Nak Young Chong, Marc P. Deisenroth (2016). Resource-Constrained Decentralized Active Sensing using Distributed Gaussian Processes for Multi-Robots. Proceedings of the International Conference on Control, Automation and Systems (ICCAS).

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Stefanos Eleftheriadis, Ognjen Rudovic, Marc P. Deisenroth, Maja Pantic (2016). Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units. Proceedings of the Asian Conference on Computer Vision (ACCV).

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Benjamin P. Chamberlain, Josh Levy-Kramer, Clive Humby, Marc P. Deisenroth (2016). Real-Time Community Detection in Large Social Networks on a Laptop. International Workshop on Mining and Learning with Graphs.

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Matthew C. H. Lee, Hugh Salimbeni, Marc P. Deisenroth, Ben Glocker (2016). Patch Kernels for Gaussian Processes in High-Dimensional Imaging Problems. NIPS Workshop on Practical Bayesian Nonparametrics.

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Roberto Calandra, Jan Peters, Carl E. Rasmussen, Marc P. Deisenroth (2016). Manifold Gaussian Processes for Regression. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN).

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Maciej Kurek, Marc P. Deisenroth, Wayne Luk, Timothy Todman (2016). Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs. Proceedings of the IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM).

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Hugh Salimbeni, Marc P. Deisenroth (2016). Gaussian Process Multiclass Classification with Dirichlet Priors for Imbalanced Data. NIPS Workshop on Practical Bayesian Nonparametrics.

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Doniyor Ulmasov, Caroline Baroukh, Benoit Chachuat, Marc P. Deisenroth, Ruth Misener (2016). Bayesian Optimization with Dimension Scheduling: Application to Biological Systems. Proceedings of the European Symposium on Computer Aided Process Engineering (ESCAPE).

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Roberto Calandra, André Seyfarth, Jan Peters, Marc P. Deisenroth (2016). Bayesian Optimization for Learning Gaits under Uncertainty. Annals in Mathematics and Artificial Intelligence.

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Marc P. Deisenroth, Jun W. Ng (2015). Robust Bayesian Committee Machine for Large-Scale Gaussian Processes. Large-Scale Kernel Machines Workshop at ICML 2015.

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Roberto Calandra, Serena Ivaldi, Marc P. Deisenroth, Jan Peters (2015). Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin. Proceedings of the IEEE-RAS International Conference on Humanoid Robots (HUMANOIDS).

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Roberto Calandra, Serena Ivaldi, Marc P. Deisenroth, Elmar Rueckert, Jan Peters (2015). Learning Inverse Dynamics Models with Contacts. Proceedings of the IEEE International Conference on Robotics and Automation.

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Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth (2015). Learning Deep Dynamical Models From Image Pixels. Proceedings of the IFAC Symposium on System Identification (SYSID).

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Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen (2015). Gaussian Processes for Data-Efficient Learning in Robotics and Control. IEEE Transactions on Pattern Analysis and Machine Intelligence.

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Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth (2015). From Pixels to Torques: Policy Learning with Deep Dynamical Models. Deep Learning Workshop at ICML 2015.

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Marc P. Deisenroth, Jun W. Ng (2015). Distributed Gaussian Processes. Proceedings of the International Conference on Machine Learning (ICML).

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John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth (2015). Data-efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. arXiv:1510.02173.

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Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois Knoll, Jan Peters, Marc P. Deisenroth (2014). Policy Search For Learning Robot Control Using Sparse Data. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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Roberto Calandra, Jan Peters, Marc P. Deisenroth (2014). Pareto Front Modeling for Sensitivity Analysis in Multi-Objective Bayesian Optimization. Workshop on Bayesian Optimization in Academia and Industry at NIPS 2014.

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Marc P. Deisenroth, Peter Englert, Jan Peters, Dieter Fox (2014). Multi-Task Policy Search for Robotics. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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Sanket Kamthe, Jan Peters, Marc P. Deisenroth (2014). Multi-Modal Filtering for Non-linear Estimation. International Conference on Acoustics, Speech, and Signal Processing (ICASSP).

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Arunkumar Byravan, Diete Fox, Marc P. Deisenroth (2014). Model-based Inverse Reinforcement Learning. Workshop on Autonomously Learning Robots at NIPS 2014.

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Niklas Wahlström, Thomas B. Schön, Marc P. Deisenroth (2014). Learning Deep Dynamical Models From Image Pixels. arXiv:1410.7550.

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Roberto Calandra, Nakul Gopalan, André Seyfarth, Jan Peters, Marc P. Deisenroth (2014). Bayesian Gait Optimization for Bipedal Locomotion. Proceedings of the International Conference on Learning and Intelligent Optimization (LION).

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Nooshin HajiGhassemi, Marc P. Deisenroth (2014). Approximate Inference for Long-Term Forecasting with Periodic Gaussian Processes. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Roberto Calandra, Jan Peters, André Seyfarth, Marc P. Deisenroth (2014). An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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Zhikun Wang, Katharina Mülling, Marc P. Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters (2013). Probabilistic Movement Modeling for Intention-based Decision Making. International Journal of Robotics Research (IJRR).

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Peter Englert, Alexandros Paraschos, Jan Peters, Marc Deisenroth (2013). Probabilistic Model-based Imitation Learning. Adaptive Behavior.

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Peter Englert, Alexandros Paraschos, Jan Peters, Marc P. Deisenroth (2013). Model-based Imitation Learning by Probabilistic Trajectory Matching. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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Marc P. Deisenroth, Peter Engert, Alexandros Paraschos, Jan Peters (2013). Imitation Learning by Model-based Probabilistic Trajectory Matching. Workshop on Machine Learning and Cognitive Science.

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Gerhard Neumann, Christian Daniel, Andras Kupcsik, Marc P. Deisenroth, Jan Peters (2013). Hierarchical Learning of Motor Skills with Information-Theoretic Policy Search. European Workshop on Reinforcement Learning (EWRL).

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Nakul Gopalan, Marc P. Deisenroth, Jan Peters (2013). Feedback Error Learning for Rhythmic Motor Primitives. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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Andras Kupcsik, Marc P. Deisenroth, Jan Peters, Gerhard Neumann (2013). Data-Efficient Generalization of Robot Skills with Contextual Policy Search. Proceedings of the AAAI Conference on Artificial Intelligence.

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Roberto Calandra, Jan Peters, André Seyfarth, Marc P. Deisenroth (2013). An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion. Workshop on Bayesian Optimization at NIPS 2013.

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Peter Englert, Alexandros Paraschos, Jan Peters, Marc P. Deisenroth (2013). Addressing the Correspondence Problem by Model-based Imitation Learning. ICRA Workshop on Autonomous Learning.

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Marc P. Deisenroth, Gerhard Neumann, Jan Peters (2013). A Survey on Policy Search for Robotics. Foundations and Trends in Robotics.

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Marc P. Deisenroth, Shakir Mohamed (2012). Expectation Propagation in Gaussian Process Dynamical Systems. Advances in Neural Information Processing Systems (NIPS).

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Marc P. Deisenroth, Roberto Calandra, André Seyfarth, Jan Peters (2012). Toward Fast Policy Search for Learning Legged Locomotion. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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Marc P. Deisenroth, Jan Peters (2012). Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise. European Workshop on Reinforcement Learning (EWRL).

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Marc P. Deisenroth, Ryan Turner, Marco Huber, Uwe D. Hanebeck, Carl E. Rasmussen (2012). Robust Filtering and Smoothing with Gaussian Processes. IEEE Transactions on Automatic Control (IEEE-TAC).

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Marc P. Deisenroth, Csaba Szepesvári, Jan Peters (2012). Proceedings of the 10th European Workshop on Reinforcement Learning.

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Zhikun Wang, Marc P. Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters (2012). Probabilistic Modeling of Human Dynamics for Intention Inference. Proceedings of Robotics: Science & Systems (RSS).

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Roberto Calandra, Tapani Raiko, Marc P. Deisenroth, Federico Montesino Pouzols (2012). Learning Deep Belief Networks from Non-Stationary Streams. Proceedings of International Conference on Artificial Neural Networks (ICANN).

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Marc P. Deisenroth, Peter Englert, Alexandros Paraschos, Jan Peters, Carl E. Rasmussen, Dieter Fox (2012). Autonomous Planning and Control with Bayesian Nonparametric Models.

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Marc P. Deisenroth, Carl E. Rasmussen (2011). PILCO: A Model-Based and Data-Efficient Approach to Policy Search. Proceedings of the International Conference on Machine Learning (ICML).

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Marc P. Deisenroth, Dieter Fox (2011). Multiple-Target Reinforcement Learning with a Single Policy.

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Marc P. Deisenroth, Carl E. Rasmussen, Dieter Fox (2011). Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning. Proceedings of the International Conference on Robotics: Science and Systems (RSS).

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Marc P. Deisenroth, Dieter Fox, Carl E. Rasmussen (2011). Learning in Robotics using Bayesian Nonparametrics.

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Cynthia Matuszek, Brian Mayton, Roberto Aimi, Marc P. Deisenroth, Liefeng Bo, Robert Chu, Michael Kung, Louis LeGrand, Joshua R. Smith, Dieter Fox (2011). Gambit: An Autonomous Chess-Playing Robotic System. Proceedings of the International Conference on Robotics and Automation (ICRA).

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Marc P. Deisenroth, Henrik Ohlsson (2011). A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving New Algorithms. Proceedings of the American Control Conference (ACC).

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Ryan Turner, Marc P. Deisenroth, Carl E. Rasmussen (2010). State-Space Inference and Learning with Gaussian Processes. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS).

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Marc P. Deisenroth (2010). Efficient Reinforcement Learning using Gaussian Processes.

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Marc P. Deisenroth, Carl E. Rasmussen, Jan Peters (2009). Gaussian Process Dynamic Programming. Neurocomputing.

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Marc P. Deisenroth (2009). Efficient Reinforcement Learning using Gaussian Processes.

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Marc P. Deisenroth, Carl E. Rasmussen (2009). Efficient Reinforcement Learning for Motor Control. Proceedings of the 10th International Workshop on Systems and Control.

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Marc P. Deisenroth, Carl E. Rasmussen (2009). Bayesian Inference for Efficient Learning in Control. Multidisciplinary Symposium on Reinforcement Learning (MSRL).

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Marc P. Deisenroth, Marco F. Huber, Uwe D. Hanebeck (2009). Analytic Moment-based Gaussian Process Filtering. Proceedings of the 26th International Conference on Machine Learning (ICML).

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Carl E. Rasmussen, Marc P. Deisenroth (2008). Probabilistic Inference for Fast Learning in Control. European Workshop on Reinforcement Learning.

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Marc P. Deisenroth, Carl E. Rasmussen, Jan Peters (2008). Model-Based Reinforcement Learning with Continuous States and Actions. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN).

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Marc P. Deisenroth, Jan Peters, Carl E. Rasmussen (2008). Approximate Dynamic Programming with Gaussian Processes. Proceedings of the 2008 American Control Conference (ACC).

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Marc P. Deisenroth, Florian Weissel, Toshiyuki Ohtsuka, Uwe D. Hanebeck (2007). Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces. Proceedings of the European Control Conference 2007 (ECC).

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Marc P. Deisenroth, Toshiyuki Ohtsuka, Florian Weissel, Dietrich Brunn, Uwe D. Hanebeck (2006). 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).

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Marc P. Deisenroth (2006). An Online Computation Approach to Optimal Finite-Horizon Control of Nonlinear Stochastic Systems.

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Marc P. Deisenroth (2004). Toward Optimal Control of Nonlinear Systems with Continuous State Spaces.

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