Publications

(2021). Matérn Gaussian Processes on Graphs. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2021). Learning Contact Dynamics using Physically Structured Neural Networks. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2021). Aligning Time Series on Incomparable Spaces. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).

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(2020). Probabilistic Active Meta-Learning. Advances in Neural Information Processing Systems (NeurIPS).

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

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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. Bayesian Deep Learning Workshop at NeurIPS.

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

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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). 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). 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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(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). Bayesian Optimization with Dimension Scheduling: Application to Biological Systems. Proceedings of the European Symposium on Computer Aided Process Engineering (ESCAPE).

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

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

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

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

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

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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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(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). 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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(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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