Publications

Tiago Silva, Daniel Augusto De Souza, Diego Mesquita (2024). Streaming Bayes GFlowNets. Advances in Neural Information Processing Systems.

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Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc P. Deisenroth (2024). Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. Advances in Neural Information Processing Systems (NeurIPS).

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Joel Oskarsson, Tomas Landelius, Marc P. Deisenroth, Fredrik Lindsten (2024). Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. Advances in Neural Information Processing Systems (NeurIPS).

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Sicelukwanda Zwane, Daniel G. Cheney, Curtis C. Johnson, Yicheng Luo, Yasemin Bekiroğlu, Marc Killpack, Marc P. Deisenroth (2024). Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials. Proceedings of the International Conference on Intelligent Robots and Systems (IROS).

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William Gregory, Ronald MacEachern, So Takao, Isobel Lawrence, Carmen Nab, Marc P. Deisenroth, Michel Tsamados (2024). Scalable Interpolation of Satellite Altimetry Data with Probabilistic Machine Learning. Nature Communications.

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Mathieu Alain, So Takao, Brooks Paige, Marc P. Deisenroth (2024). Gaussian Processes on Cellular Complexes. Proceedings of the International Conference on Machine Learning (ICML).

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Rafael Anderka, Marc P. Deisenroth, So Takao (2024). Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

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Oscar Key, So Takao, Daniel Giles, Marc P. Deisenroth (2024). Scalable Data Assimilation with Message Passing. Environmental Data Science.

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Vignesh Gopakumar, Stanislas Pamela, Lorenzo Zanisi, Zongyi Li, Ander Gray, Daniel Brennand, Nitesh Bhatia, Gregory Stathopoulos, Matt Kusner, Marc P. Deisenroth, Anima Anandkumar (2024). Plasma Surrogate Modelling using Fourier Neural Operators. Nuclear Fusion.

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Lucas Cosier, Rares Iordan, Sicelukwanda Zwane, Giovanni Franzese, James T. Wilson, Marc P. Deisenroth, Alexander Terenin, Yasemin Bekiroğlu (2024). A Unifying Variational Framework for Gaussian Process Motion Planning. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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Daniel Augusto De Souza, Daniel Giles, Marc P. Deisenroth (2024). Interpretable Deep Gaussian Processes for Geospatial Tasks. Climate Informatics.

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Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, Serge Guillas (2024). ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package. Geoscientific Model Development.

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Daniel Augusto De Souza, Alexander Nikitin, S. T. John, Magnus Ross, Mauricio A. Álvarez, Marc P. Deisenroth, João P. P. Gomes, Diego Mesquita, César Lincoln Mattos (2023). Thin and Deep Gaussian Processes. Advances in Neural Information Processing Systems.

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Yiting Chen, Ahmet E. Tekden, Marc P. Deisenroth, Yasemin Bekiroğlu (2023). Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-finger Hands. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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Ahmet Tekden, Marc P. Deisenroth, Yasemin Bekiroğlu (2023). Neural Field Movement Primitives for Joint Modelling of Scenes and Motions. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

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Sicelukwanda Zwane, Denis Hadjivelichkov, Yicheng Luo, Yasemin Bekiroğlu, 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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Alexander L. I. Norcliffe, Marc P. Deisenroth (2023). Faster Training of Neural ODEs Using Gauß–Legendre Quadrature. Transactions on Machine Learning Research (TMLR).

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Ahmet Tekden, Marc P. Deisenroth, Yasemin Bekiroğlu (2023). Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces. IEEE Robotics and Automation Letters (RA-L).

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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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Mihaela C. Rosca (2023). On discretisation drift and smoothness regularisation in neural network training.

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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 Bekiroğlu, 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 Augusto de Souza, So Takao, Mark 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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James T. Wilson (2023). Decision-Making with Gaussian Processes: Sampling Strategies and Monte Carlo Methods.

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Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, Marı́a Pérez-Ortiz, Sahan Bulathwela (2023). TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback. ORSUM RecSys Workshop.

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Nicolas Belissent, Jose Manuel Peña, Gustavo Mesı́as-Ruiz, John, Pérez-Ortiz, Maria Shawe-Taylor (2023). Transfer and zero-shot learning for weed species detection with small datasets and unseen classes. 14th European Conference on Precision Agriculture (ECPA).

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Sahan Bulathwela, Marı́a Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor (2023). Leveraging Semantic Knowledge Graphs in Educational Recommenders to Address the Cold-Start Problem. Semantic AI in Knowledge Graphs.

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Gustavo a Mesı́as-Ruiz, Mar\á Pérez-Ortiz, José Dorado, Ana I De Castro, José M Peña (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Frontiers in Plant Science.

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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 Bekiroğlu (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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Maria Perez-Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, Emine Yilmaz (2022). Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?. ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR).

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Sahan Bulathwela, Marı́a Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor (2022). Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources. Sustainability.

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Daniel Augusto 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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Marı́a Pérez-Ortiz, Petru Manescu, Fabio Caccioli, Delmiro Fernández-Reyes, Parashkev Nachev, John Shawe-Taylor (2022). Network topological determinants of pathogen spread. Scientific Reports.

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Stefan Schlobach, Marı́a Pérez-Ortiz, Myrthe Tielman (2022). HHAI2022: Augmenting Human Intellect: Proceedings of the First International Conference on Hybrid Human-Artificial Intelligence. IOS Press.

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Ben Dixon, Jacob Bieker, Marı́a Pérez-Ortiz (2022). Comparing the carbon costs and benefits of low-resource solar nowcasting. NeurIPS Workshop on Tackling Climate Change with Machine Learning.

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Hadi Hajieghrary, Marc P. Deisenroth, Yasemin Bekiroğlu (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 Bekiroğlu (2021). Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects. IEEE Robotics and Automation Letters.

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Marı́a Pérez-Ortiz, Claire Dormann, Yvonne Rogers, Sahan Bulathwela, Stefan Kreitmayer, Emine Yilmaz, Richard Noss, John Shawe-Taylor (2021). X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI. 26th International Conference on Intelligent User Interfaces.

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Marı́a Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári (2021). Tighter Risk Certificates for Neural Networks. Journal of Machine Learning Research.

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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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Sahan Bulathwela, Marı́a Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor (2021). Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems. KGSWC'21 Workshop on Joint Use of Probabilistic Graphical Models and Ontologies.

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Tom R Andersson, J Scott Hosking, Maria Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Daniel C Jones, Jeremy Wilkinson, Tony Phillips, Others (2021). Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications.

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Maria Perez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernandez, Benjamin Guedj, John Shawe-Taylor (2021). Progress in Self-Certified Neural Networks. NeurIPS workshop on Bayesian Deep Learning.

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Sahan Bulathwela, Maria Perez-Ortiz, Erik Novak, Emine Yilmaz, John Shawe-Taylor (2021). PEEK: A Large Dataset of Learner Engagement with Educational Videos. ACM RecSys'21 Workshop on Online Recommender Systems and User Modeling.

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Marı́a Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler (2021). Learning PAC-Bayes Priors for Probabilistic Neural Networks. arXiv preprint arXiv:2109.10304.

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Sahan Bulathwela, Marı́a Pérez-Ortiz, Catherine Holloway, John Shawe-Taylor (2021). Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution. NeurIPS workshop on ML for the Developing World.

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Tom R Andersson, J Scott Hosking, Eleanor Krige, Maria Pérez-Ortiz, Brooks Paige, Andrew Elliott, Chris Russell, Stephen Law, Daniel C Jones, Jeremy Wilkinson, Others (2021). A daily to seasonal Arctic sea ice forecasting AI.

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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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Sahan Bulathwela, Stefan Kreitmayer, Marı́a Pérez-Ortiz (2020). What's in it for me? Augmenting Recommended Learning Resources with Navigable Annotations. 25th International Conference on Intelligent User Interfaces.

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Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor (2020). VLEngagement: A dataset of scientific video lectures for evaluating population-based engagement. arXiv preprint arXiv:2011.02273.

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Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor (2020). TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. 2020 AAAI Conference on Artificial Intelligence.

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Marıa Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári (2020). Towards self-certified learning: Probabilistic neural networks trained by PAC-Bayes with Backprop. NeurIPS workshop on "Beyond Backpropagation".

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Sahan Bulathwela, Marı́a Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin De La Higuera, John Shawe-Taylor, Emine Yilmaz (2020). SUM'20: State-based User Modelling. 13th International Conference on Web Search and Data Mining (WSDM).

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Sophie Wuerger, Maliha Ashraf, Minjung Kim, Jasna Martinovic, Marıa Pérez-Ortiz, Rafał K Mantiuk (2020). Spatio-chromatic contrast sensitivity under mesopic and photopic light levels. Journal of Vision.

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Sahan Bulathwela, Marı́a Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor (2020). Predicting Engagement in Video Lectures. International Conference on Educational Data Mining.

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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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Tom Andersson, Fruzsina Agocs, Scott Hosking, Marı́a Pérez-Ortiz, Brooks Paige, Chris Russell, Andrew Elliott, Stephen Law, Jeremy Wilkinson, Yevgeny Askenov, Others (2020). Deep learning for monthly Arctic sea ice concentration prediction. 22nd European Geosciences Union (EGU) General Assembly.

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Minjung Kim, Maliha Ashraf, Marıa Pérez-Ortiz, Jasna Martinovic, Sophie Wuerger, Rafał K Mantiuk (2020). Contrast Sensitivity Functions for HDR Displays. London Imaging Meeting.

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Aliaksei Mikhailiuk, Maria Perez-Ortiz, Dingcheng Yue, Wilson Suen, Rafal K Mantiuk (2020). Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality. IEEE Transactions on Multimedia.

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Aliaksei Mikhailiuk, Clifford Wilmot, Maria Perez-Ortiz, Dingcheng Yue, Rafal Mantiuk (2020). Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization. International Conference on Pattern Recognition (ICPR).

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Théophile Cantelobre, Benjamin Guedj, Marı́a Pérez-Ortiz, John Shawe-Taylor (2020). A PAC-Bayesian perspective on structured prediction with implicit loss embeddings. arXiv preprint arXiv:2012.03780.

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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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Nanyang Ye, Marıa Pérez-Ortiz, Rafał K Mantiuk (2019). Visibility Metric for Visually Lossless Image Compression. Picture Coding Symposium.

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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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Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor (2019). Towards an Integrative Educational Recommender for Lifelong Learners. 2020 AAAI Conference on Artificial Intelligence.

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Javier Sánchez-Monedero, Pedro Antonio Gutiérrez, Marı́a Pérez-Ortiz (2019). Orca: A matlab/octave toolbox for ordinal regression. Journal of Machine Learning Research.

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Marı́a Pérez-Ortiz, Antonio Manuel Durán-Rosal, Pedro Antonio Gutiérrez, Javier Sanchez-Monedero, Athanasia Nikolaou, Francisco Fernández-Navarro, César Hervás-Mart\ńez (2019). On the use of evolutionary time series analysis for segmenting paleoclimate data. Neurocomputing.

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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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Maria Perez-Ortiz, Aliaksei Mikhailiuk, Emin Zerman, Vedad Hulusic, Giuseppe Valenzise, Rafał K Mantiuk (2019). From pairwise comparisons and rating to a unified quality scale. IEEE Transactions on Image Processing.

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Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk, Cesar Hervas-Martinez (2019). Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets. AAAI conference.

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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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Marı́a Dolores Ayllón, Rubén Ciria, Manuel Cruz-Ram\ŕez, Mar\' ́And Gómez, Irene Pérez-Ortiz, Roberto Valente, John O'Grady, Manuel De La Mata, César Hervás-Mart\'ıź, Nigel D Heaton, Others (2018). Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transplantation.

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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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Aliaksei Mikhailiuk, Maria Perez-Ortiz, Rafal K. Mantiuk (2018). Psychometric scaling of TID2013 dataset. Quality of Multimedia Experience.

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Javier Sánchez-Monedero, Marı́a Pérez-Ortiz, Aurora Saez, Pedro Antonio Gutiérrez, César Hervás-Mart\ńez (2018). Partial order label decomposition approaches for melanoma diagnosis. Applied Soft Computing.

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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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Ricardo Cruz, Kelwin Fernandes, Joaquim F Pinto Costa, Marı́a Pérez-Ortiz, Jaime S Cardoso (2018). Binary ranking for ordinal class imbalance. Pattern Analysis and Applications.

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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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M. Perez-Ortiz, P.A. Gutierrez, P. Tino, C. Casanova-Mateo, S. Salcedo-Sanz (2018). A mixture of experts model for predicting persistent weather patterns. International Joint Conference on Neural Networks.

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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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M Pérez-Ortiz, Pedro Antonio Gutiérrez, Marı́a Dolores Ayllón-Terán, N Heaton, Rubén Ciria, Javier Briceño, César Hervás-Mart\ńez (2017). Synthetic semi-supervised learning in imbalanced domains: Constructing a model for donor-recipient matching in liver transplantation. Knowledge-Based Systems.

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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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Ricardo Cruz, Kelwin Fernandes, Joaquim F. Pinto Costa, M. Pérez-Ortiz, Jaime S. Cardoso (2017). Ordinal Class Imbalance with Ranking. Proceedings of Iberian Conference on Pattern Recognition and Image Analysis.

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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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Manuel Dorado-Moreno, Marı́a Pérez-Ortiz, Pedro a Gutiérrez, Rubén Ciria, Javier Briceño, César Hervás-Mart\ńez (2017). Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artificial Intelligence in Medicine.

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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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Ricardo Cruz, Kelwin Fernandes, Joaquim F. Pinto Costa, M. Pérez-Ortiz, Jaime S. Cardoso (2017). Combining Ranking with Traditional Methods for Ordinal Class Imbalance. International Work-Conference on Artificial Neural Networks.

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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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Maria Perez-Ortiz, Rafal Mantiuk (2017). A practical guide and software for analysing pairwise comparison experiments. CoRR, arXiv preprint.

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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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Marı́a Pérez-Ortiz, Aurora Sáez, Javier Sánchez-Monedero, Pedro Antonio Gutiérrez, César Hervás-Mart\ńez (2016). Tackling the ordinal and imbalance nature of a melanoma image classification problem. 2016 international joint conference on neural networks (IJCNN).

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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Mariano Carbonero-Ruz, César Hervás-Mart\ńez (2016). Semi-supervised learning for ordinal Kernel Discriminant Analysis. Neural Networks.

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Marı́a Pérez-Ortiz, José Manuel Peña, Pedro Antonio Gutiérrez, Jorge Torres-Sánchez, César Hervás-Mart\ńez, Francisca López-Granados (2016). Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery. Expert Systems with Applications.

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Pedro Antonio Gutiérrez, Marı́a Pérez-Ortiz, Javier Sanchez-Monedero, César Hervás-Mart\ńez (2016). Representing ordinal input variables in the context of ordinal classification. 2016 International Joint Conference on Neural Networks (IJCNN).

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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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Manuel Dorado-Moreno, Marı́a Pérez-Ortiz, Mar\á Dolores Ayllón-Terán, Pedro Antonio Gutiérrez, Cesar Hervás-Mart\'éz (2016). Ordinal evolutionary artificial neural networks for solving an imbalanced liver transplantation problem. Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016, Proceedings 11.

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Marı́a Pérez-Ortiz, Manuel Fernández-Delgado, Eva Cernadas, Rosario Dom\ńguez-Petit, Pedro Antonio Gutiérrez, César Hervás-Mart\'éz (2016). On the use of nominal and ordinal classifiers for the discrimination of states of development in fish oocytes. Neural Processing Letters.

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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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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, José M Peña, Jorge Torres-Sánchez, Francisca López-Granados, César Hervás-Mart\ńez (2016). Machine learning paradigms for weed mapping via unmanned aerial vehicles. 2016 IEEE symposium series on computational intelligence (SSCI).

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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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Marı́a Pérez-Ortiz, Mercedes Torres-Jiménez, Pedro Antonio Gutiérrez, Javier Sánchez-Monedero, César Hervás-Mart\ńez (2016). Fisher score-based feature selection for ordinal classification: A social survey on subjective well-being. Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016, Proceedings 11.

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Javier Sánchez-Monedero, Aurora Sáez, Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Cesar Hervás-Mart\ńez (2016). Classification of melanoma presence and thickness based on computational image analysis. Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016, Proceedings 11.

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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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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Javier Sanchez-Monedero, César Hervás-Mart\ńez (2016). A study on multi-scale kernel optimisation via centered kernel-target alignment. Neural Processing Letters.

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Marı́a Pérez-Ortiz, Silvia Jiménez-Fernández, Pedro a Gutiérrez, Enrique Alexandre, César Hervás-Mart\ńez, Sancho Salcedo-Sanz (2016). A review of classification problems and algorithms in renewable energy applications. Energies.

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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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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Peter Tino, César Hervás-Mart\ńez (2015). Oversampling the Minority Class in the Feature Space. IEEE Transactions on Neural Networks and Learning Systems.

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Pedro Antonio Gutiérrez, Maria Perez-Ortiz, Javier Sanchez-Monedero, Francisco Fernandez-Navarro, Cesar Hervas-Martinez (2015). Ordinal regression methods: survey and experimental study. IEEE Transactions on Knowledge and Data Engineering.

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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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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Manuel Cruz-Ram\ŕez, Javier Sanchez-Monedero, César Hervás-Mart\'éz (2015). Kernelising the Proportional Odds Model through kernel learning techniques. Neurocomputing.

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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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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Jose Manuel Peña, Jorge Torres-Sánchez, César Hervás-Mart\ńez, Francisca López-Granados (2015). An experimental comparison for the identification of weeds in sunflower crops via unmanned aerial vehicles and object-based analysis. Advances in Computational Intelligence: 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015. Proceedings, Part I 13.

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Maria Pérez-Ortiz, JM Peña, Pedro Antonio Gutiérrez, Jorge Torres-Sánchez, César Hervás-Martı́nez, Francisca López-Granados (2015). A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing.

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M Pérez-Ortiz, Pedro Antonio Gutiérrez, Javier Sánchez-Monedero, César Hervás-Martı́nez, Athanasia Nikolaou, Isabelle Dicaire, Francisco Fernández-Navarro (2014). Time series segmentation of paleoclimate tipping points by an evolutionary algorithm. Hybrid Artificial Intelligence Systems: 9th International Conference, HAIS 2014, Salamanca, Spain, June 11-13, 2014. Proceedings 9.

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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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Maria Perez-Ortiz, Pedro Antonio Gutierrez, Cesar Hervas-Martinez, Xin Yao (2014). Graph-based approaches for over-sampling in the context of ordinal regression. IEEE Transactions on Knowledge and Data Engineering.

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Marı́a Pérez-Ortiz, M De La Paz-Mar\ń, Pedro Antonio Gutiérrez, César Hervás-Mart\'éz (2014). Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques. Knowledge-Based Systems.

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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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M Pérez-Ortiz, Manuel Cruz-Ramı́rez, Mar\á Dolores Ayllón-Terán, N Heaton, Rubén Ciria, César Hervás-Mart\'éz (2014). An organ allocation system for liver transplantation based on ordinal regression. Applied Soft Computing.

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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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Laura Garcı́a-Hernández, M Pérez-Ortiz, Antonio Arauzo-Azofra, Lorenzo Salas-Morera, César Hervás-Mart\ńez (2014). An evolutionary neural system for incorporating expert knowledge into the UA-FLP. Neurocomputing.

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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, César Hervás-Mart\ńez (2013). Projection-Based Ensemble Learning for Ordinal Regression. IEEE Transactions on Cybernetics.

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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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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Javier Sánchez-Monedero, César Hervás-Mart\ńez (2013). Multi-scale Support Vector Machine Optimization by Kernel Target-Alignment.. ESANN.

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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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Manuel Cruz-Ramı́rez, César Hervás-Mart\ńez, Pedro Antonio Gutiérrez, Mar\' ́And Briceño, Javier Pérez-Ortiz, Manuel De La Mata (2013). Memetic Pareto differential evolutionary neural network used to solve an unbalanced liver transplantation problem. Soft Computing.

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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, Manuel Cruz-Ram\ŕez, Javier Sánchez-Monedero, Cesar Hervás-Mart\'éz (2013). Kernelizing the proportional odds model through the empirical kernel mapping. Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part I 12.

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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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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, César Hervás-Mart\ńez (2013). Borderline kernel based over-sampling. Hybrid Artificial Intelligent Systems: 8th International Conference, HAIS 2013, Salamanca, Spain, September 11-13, 2013. Proceedings 8.

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Javier Sánchez-Monedero, Pedro Antonio Gutiérrez, Marı́a Pérez-Ortiz, César Hervás-Mart\ńez (2013). An n-spheres based synthetic data generator for supervised classification. Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Proceedings, Part I 12.

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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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Pedro Antonio Gutiérrez, M Pérez-Ortiz, Francisco Fernández-Navarro, Javier Sánchez-Monedero, César Hervás-Martı́nez (2012). An experimental study of different ordinal regression methods and measures. Hybrid Artificial Intelligent Systems: 7th International Conference, HAIS 2012, Salamanca, Spain, March 28-30th, 2012. Proceedings, Part II 7.

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Marı́a Pérez-Ortiz, Pedro Antonio Gutiérrez, César Hervás-Mart\ńez, Javier Briceño, Manuel De La Mata (2012). An ensemble approach for ordinal threshold models applied to liver transplantation. The 2012 International Joint Conference on Neural Networks (IJCNN).

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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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M Pérez-Ortiz, Pedro Antonio Gutiérrez, C Garcı́a-Alonso, Luis Salvador-Carulla, Jose Alberto Salinas-Perez, César Hervás-Mart\ńez (2011). Ordinal classification of depression spatial hot-spots of prevalence. 2011 11th International Conference on Intelligent Systems Design and Applications.

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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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