Publications

(2020). Stochastic Differential Equations with Variational Wishart Diffusions. Proceedings of the International Conference on Machine Learning (ICML).

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(2020). Healing Products of Gaussian Process Experts. Proceedings of the International Conference on Machine Learning (ICML).

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(2020). Variational Integrator Networks for Physically Structured Embeddings. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2020). Asynchronous Gibbs Sampling. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2020). Mathematics for Machine Learning. Cambridge University Press.

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(2019). High-Dimensional Bayesian Optimization Using Low-Dimensional Feature Spaces. Bayesian Deep Learning Workshop at NeurIPS.

(2019). Disentangled Skill Embeddings for Reinforcement Learning. NeurIPS Workshop on Learning Transferable Skills.

(2019). Variational Integrator Networks. Bayesian Deep Learning Workshop at NeurIPS.

(2019). Fast Decomposable Submodular Function Minimization using Constrained Total Variation. Advances in Neural Information Processing Systems.

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(2019). Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms. arXiv:1905.04873.

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(2019). Deep Gaussian Processes with Importance-Weighted Variational Inference. Proceedings of the International Conference on Machine Learning (ICML).

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(2019). Variational Integrator Networks for Physically Meaningful Embeddings. arXiv:1910.09349.

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(2019). High-Dimensional Bayesian Optimization with Manifold Gaussian Processes. arXiv:1902.10675.

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(2018). Orthogonally Decoupled Variational Gaussian Processes. Advances in Neural Information Processing Systems (NeurIPS).

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(2018). Maximizing Acquisition Functions for Bayesian Optimization. Advances in Neural Information Processing Systems (NeurIPS).

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(2018). Meta Reinforcement Learning with Latent Variable Gaussian Processes. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI).

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(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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(2018). Real-Time Community Detection in Full Social Networks on a Laptop. PLOS ONE.

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(2018). Gaussian Process Conditional Density Estimation. Advances in Neural Information Processing Systems (NeurIPS).

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(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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(2017). Doubly Stochastic Variational Inference for Deep Gaussian Processes. Advances in Neural Information Processing Systems (NIPS).

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(2017). The Reparameterization Trick for Acquisition Functions. NIPS Workshop on Bayesian Optimization.

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(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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(2017). Neural Embeddings of Graphs in Hyperbolic Space. International Workshop on Mining and Learning with Graphs.

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(2017). Identification of Gaussian Process State Space Models. Advances in Neural Information Processing Systems (NIPS).

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(2017). Deeply Non-Stationary Gaussian Processes. NIPS Workshop on Bayesian Deep Learning.

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(2017). Customer Life Time Value Prediction Using Embeddings. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD).

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(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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(2017). A Brief Survey of Deep Reinforcement Learning. IEEE Signal Processing Magazine.

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(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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(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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(2016). Real-Time Community Detection in Large Social Networks on a Laptop. International Workshop on Mining and Learning with Graphs.

(2016). Patch Kernels for Gaussian Processes in High-Dimensional Imaging Problems. NIPS Workshop on Practical Bayesian Nonparametrics.

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(2016). Manifold Gaussian Processes for Regression. Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN).

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(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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(2016). Gaussian Process Multiclass Classification with Dirichlet Priors for Imbalanced Data. NIPS Workshop on Practical Bayesian Nonparametrics.

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(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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(2016). Bayesian Optimization for Learning Gaits under Uncertainty. Annals in Mathematics and Artificial Intelligence.

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(2015). Robust Bayesian Committee Machine for Large-Scale Gaussian Processes. Large-Scale Kernel Machines Workshop at ICML 2015.

(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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(2015). Learning Inverse Dynamics Models with Contacts. Proceedings of the IEEE International Conference on Robotics and Automation.

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(2015). Learning Deep Dynamical Models From Image Pixels. Proceedings of the IFAC Symposium on System Identification (SYSID).

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(2015). Gaussian Processes for Data-Efficient Learning in Robotics and Control. IEEE Transactions on Pattern Analysis and Machine Intelligence.

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(2015). From Pixels to Torques: Policy Learning with Deep Dynamical Models. Deep Learning Workshop at ICML 2015.

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(2015). Distributed Gaussian Processes. Proceedings of the International Conference on Machine Learning (ICML).

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(2015). Data-efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. arXiv:1510.02173.

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(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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(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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(2014). Multi-Task Policy Search for Robotics. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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(2014). Multi-Modal Filtering for Non-linear Estimation. International Conference on Acoustics, Speech, and Signal Processing (ICASSP).

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(2014). Model-based Inverse Reinforcement Learning. Workshop on Autonomously Learning Robots at NIPS 2014.

(2014). Learning Deep Dynamical Models From Image Pixels. arXiv:1410.7550.

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(2014). Bayesian Gait Optimization for Bipedal Locomotion. Proceedings of the International Conference on Learning and Intelligent Optimization (LION).

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(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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(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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(2013). Probabilistic Movement Modeling for Intention-based Decision Making. International Journal of Robotics Research (IJRR).

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(2013). Model-based Imitation Learning by Probabilistic Trajectory Matching. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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(2013). Imitation Learning by Model-based Probabilistic Trajectory Matching. Workshop on Machine Learning and Cognitive Science.

(2013). Feedback Error Learning for Rhythmic Motor Primitives. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).

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(2013). Data-Efficient Generalization of Robot Skills with Contextual Policy Search. Proceedings of the AAAI Conference on Artificial Intelligence.

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(2013). An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion. Workshop on Bayesian Optimization at NIPS 2013.

(2013). A Survey on Policy Search for Robotics. Foundations and Trends in Robotics.

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

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(2012). Robust Filtering and Smoothing with Gaussian Processes. IEEE Transactions on Automatic Control (IEEE-TAC).

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(2012). Probabilistic Modeling of Human Dynamics for Intention Inference. Proceedings of Robotics: Science & Systems (RSS).

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(2012). Learning Deep Belief Networks from Non-Stationary Streams. Proceedings of International Conference on Artificial Neural Networks (ICANN).

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(2012). Expectation Propagation in Gaussian Process Dynamical Systems. Advances in Neural Information Processing Systems (NIPS).

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(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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(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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(2011). Learning in Robotics using Bayesian Nonparametrics.

(2011). Gambit: An Autonomous Chess-Playing Robotic System. Proceedings of the International Conference on Robotics and Automation (ICRA).

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(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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(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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(2009). Gaussian Process Dynamic Programming. Neurocomputing.

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(2009). Efficient Reinforcement Learning for Motor Control. Proceedings of the 10th International Workshop on Systems and Control.

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(2009). Bayesian Inference for Efficient Learning in Control. Multidisciplinary Symposium on Reinforcement Learning (MSRL).

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(2009). Analytic Moment-based Gaussian Process Filtering. Proceedings of the 26th International Conference on Machine Learning (ICML).

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(2008). Probabilistic Inference for Fast Learning in Control. European Workshop on Reinforcement Learning.

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(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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(2008). Approximate Dynamic Programming with Gaussian Processes. Proceedings of the 2008 American Control Conference (ACC).

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(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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(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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