Sustainability and Machine Learning Group
Sustainability and Machine Learning Group
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Publications
Infinite Neural Operators: Gaussian Processes on Functions (2025)
Parameter Efficient Fine-tuning via Explained Variance Adaptation (2025)
Calibrated Physics-Informed Uncertainty Quantification (2025)
Semantic Cross-Pose Correspondence from a Single Example (2025)
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks (2024)
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling (2024)
Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials (2024)
Scalable Interpolation of Satellite Altimetry Data with Probabilistic Machine Learning (2024)
Gaussian Processes on Cellular Complexes (2024)
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems (2024)
Co-located OLCI Optical Imagery and SAR Altimetry from Sentinel-3 for Enhanced Arctic Spring Sea Ice Surface Classification (2024)
A Unifying Variational Framework for Gaussian Process Motion Planning (2024)
Plasma Surrogate Modelling using Fourier Neural Operators (2024)
Interpretable Deep Gaussian Processes for Geospatial Tasks (2024)
Scalable Data Assimilation with Message Passing (2024)
Challenging Systematic Prejudices: An Investigation into Bias Against Women and Girls (2024)
Thin and Deep Gaussian Processes (2023)
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions (2023)
Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-finger Hands (2023)
Safe Trajectory Sampling in Model-based Reinforcement Learning (2023)
Faster Training of Neural ODEs Using Gauß–Legendre Quadrature (2023)
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces (2023)
Understanding Deep Generative Models with Generalized Empirical Likelihoods (2023)
Queer In AI: A Case Study in Community-Led Participatory AI (2023)
Optimal Transport for Offline Imitation Learning (2023)
Actually Sparse Variational Gaussian Processes (2023)
Actually Sparse Variational Gaussian Processes (2022)
Optimal Transport for Offline Imitation Learning (2022)
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes (2022)
One-Shot Transfer of Affordance Regions? AffCorrs! (2022)
The Graph Cut Kernel for Ranked Data (2022)
Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation (2022)
Cauchy-Schwarz Regularized Autoencoder (2022)
Enhanced GPIS Learning Based on Local and Global Focus Areas (2022)
Bayesian Optimization based Nonlinear Adaptive PID Design for Robust Control of the Joints at Mobile Manipulators (2022)
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge-Equivariant Projected Kernels (2021)
Pathwise Conditioning of Gaussian Processes (2021)
Aligning Time Series on Incomparable Spaces (2021)
Learning Contact Dynamics using Physically Structured Neural Networks (2021)
Matérn Gaussian Processes on Graphs (2021)
GPflux: A Library for Deep Gaussian Processes (2021)
High-Dimensional Bayesian Optimization with Manifold Gaussian Processes (2020)
Probabilistic Active Meta-Learning (2020)
Estimating Barycenters of Measures in High Dimensions (2020)
Efficiently Sampling Functions from Gaussian Process Posteriors (2020)
Healing Products of Gaussian Process Experts (2020)
Stochastic Differential Equations with Variational Wishart Diffusions (2020)
Aligning Time Series on Incomparable Spaces (2020)
Matern Gaussian Processes on Riemannian Manifolds (2020)
Variational Integrator Networks for Physically Meaningful Embeddings (2020)
A Foliated View of Transfer Learning (2020)
Mathematics for Machine Learning (2020)
High-Dimensional Bayesian Optimization Using Low-Dimensional Feature Spaces (2019)
Variational Integrator Networks (2019)
Variational Integrator Networks for Physically Meaningful Embeddings (2019)
Deep Gaussian Processes with Importance-Weighted Variational Inference (2019)
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms (2019)
High-Dimensional Bayesian Optimization with Projections using Quantile Gaussian Processes (2019)
GPdoemd: A Python Package for Design of Experiments for Model Discrimination (2019)
Accelerating the BSM Interpretation of LHC Data with Machine Learning (2019)
Bayesian Multi-Objective Optimisation with Mixed Analytical and Black-Box Functions: Application to Tissue Engineering (2018)
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control (2018)
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches (2018)
Gaussian Process Conditional Density Estimation (2018)
Maximizing Acquisition Functions for Bayesian Optimization (2018)
Meta Reinforcement Learning with Latent Variable Gaussian Processes (2018)
Orthogonally Decoupled Variational Gaussian Processes (2018)
Real-Time Community Detection in Full Social Networks on a Laptop (2018)
A Brief Survey of Deep Reinforcement Learning (2017)
Bayesian Multi-Objective Optimisation of Neotissue Growth in a Perfusion Bioreactor Set-up (2017)
Customer Life Time Value Prediction Using Embeddings (2017)
Deeply Non-Stationary Gaussian Processes (2017)
Doubly Stochastic Variational Inference for Deep Gaussian Processes (2017)
Gaussian Process Domain Experts for Modeling of Facial Affect (2017)
Identification of Gaussian Process State Space Models (2017)
Model-based Contextual Policy Search for Data-Efficient Generalization of Robot Skills (2017)
Neural Embeddings of Graphs in Hyperbolic Space (2017)
Probabilistic Inference of Twitter Users' Age based on What They Follow (2017)
The Reparameterization Trick for Acquisition Functions (2017)
Resource-Constrained Decentralized Active Sensing using Distributed Gaussian Processes for Multi-Robots (2016)
Bayesian Optimization for Learning Gaits under Uncertainty (2016)
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems (2016)
Gaussian Process Multiclass Classification with Dirichlet Priors for Imbalanced Data (2016)
Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs (2016)
Manifold Gaussian Processes for Regression (2016)
Patch Kernels for Gaussian Processes in High-Dimensional Imaging Problems (2016)
Real-Time Community Detection in Large Social Networks on a Laptop (2016)
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units (2016)
Data-efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models (2015)
Distributed Gaussian Processes (2015)
From Pixels to Torques: Policy Learning with Deep Dynamical Models (2015)
Gaussian Processes for Data-Efficient Learning in Robotics and Control (2015)
Learning Deep Dynamical Models From Image Pixels (2015)
Learning Inverse Dynamics Models with Contacts (2015)
Learning Torque Control in Presence of Contacts using Tactile Sensing from Robot Skin (2015)
Robust Bayesian Committee Machine for Large-Scale Gaussian Processes (2015)
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion (2014)
Approximate Inference for Long-Term Forecasting with Periodic Gaussian Processes (2014)
Bayesian Gait Optimization for Bipedal Locomotion (2014)
Learning Deep Dynamical Models From Image Pixels (2014)
Model-based Inverse Reinforcement Learning (2014)
Multi-Modal Filtering for Non-linear Estimation (2014)
Multi-Task Policy Search for Robotics (2014)
Pareto Front Modeling for Sensitivity Analysis in Multi-Objective Bayesian Optimization (2014)
Policy Search For Learning Robot Control Using Sparse Data (2014)
A Survey on Policy Search for Robotics (2013)
Addressing the Correspondence Problem by Model-based Imitation Learning (2013)
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion (2013)
Data-Efficient Generalization of Robot Skills with Contextual Policy Search (2013)
Feedback Error Learning for Rhythmic Motor Primitives (2013)
Hierarchical Learning of Motor Skills with Information-Theoretic Policy Search (2013)
Imitation Learning by Model-based Probabilistic Trajectory Matching (2013)
Model-based Imitation Learning by Probabilistic Trajectory Matching (2013)
Probabilistic Model-based Imitation Learning (2013)
Probabilistic Movement Modeling for Intention-based Decision Making (2013)
Autonomous Planning and Control with Bayesian Nonparametric Models (2012)
Expectation Propagation in Gaussian Process Dynamical Systems (2012)
Learning Deep Belief Networks from Non-Stationary Streams (2012)
Probabilistic Modeling of Human Dynamics for Intention Inference (2012)
Proceedings of the 10th European Workshop on Reinforcement Learning (2012)
Robust Filtering and Smoothing with Gaussian Processes (2012)
Solving Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems with Gaussian Noise (2012)
Toward Fast Policy Search for Learning Legged Locomotion (2012)
A General Perspective on Gaussian Filtering and Smoothing: Explaining Current and Deriving New Algorithms (2011)
Gambit: An Autonomous Chess-Playing Robotic System (2011)
Learning in Robotics using Bayesian Nonparametrics (2011)
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning (2011)
Multiple-Target Reinforcement Learning with a Single Policy (2011)
PILCO: A Model-Based and Data-Efficient Approach to Policy Search (2011)
Efficient Reinforcement Learning using Gaussian Processes (2010)
State-Space Inference and Learning with Gaussian Processes (2010)
Analytic Moment-based Gaussian Process Filtering (2009)
Bayesian Inference for Efficient Learning in Control (2009)
Efficient Reinforcement Learning for Motor Control (2009)
Efficient Reinforcement Learning using Gaussian Processes (2009)
Gaussian Process Dynamic Programming (2009)
Approximate Dynamic Programming with Gaussian Processes (2008)
Model-Based Reinforcement Learning with Continuous States and Actions (2008)
Probabilistic Inference for Fast Learning in Control (2008)
Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces (2007)
An Online Computation Approach to Optimal Finite-Horizon Control of Nonlinear Stochastic Systems (2006)
Finite-Horizon Optimal State Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle (2006)
Toward Optimal Control of Nonlinear Systems with Continuous State Spaces (2004)
Seminar organization
Dan Roy: Admissibility is Bayes Optimality with Infinitesimals (Jul 2022)
Benjamin Chamberlain: A Continuous Perspective on Graph Neural Networks (Jul 2022)
Michalis Titsias: Functional Regularisation for Continual Learning with Gaussian Processes (Jun 2022)
Shahine Bouabid and Siu Chau: Deconditional Downscaling with Gaussian Processes (Feb 2022)
Geoff Pleiss: Understanding Neural Networks through Gaussian Processes, and Vice Versa (Oct 2021)
Fariba Yousefi: Multi-task Learning for Aggregated Data using Gaussian Processes (Mar 2021)
Ruha Benjamin: Reimagining the Default Settings of Technology & Society (Mar 2021)
César Lincoln C. Mattos: Probabilistic ML-Applications and Modeling Investigations (Mar 2021)
Jackie Kay: Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities (Feb 2021)
Dhruva Tirumala: Using behavior priors for data efficiency in Reinforcement Learning (Feb 2021)
Ti John: Gaussian processes for fun and profit: Probabilistic machine learning in industry (Nov 2020)
Chidubem Iddianozie: On the Prospects and Challenges of Machine Learning for Street Networks (Sep 2020)
Tim G. J. Rudner: Inter-domain Deep Gaussian Processes (Aug 2020)
Cynthia Matuszek: Robots, Language, and Environments: Modeling Linguistic Human-Robot Interactions (Jul 2020)
Christopher Jackson: 3D Seismic Reflection Data: Has the Geological Hubble Retained Its Focus? (Jun 2020)
Joanna Slawinska: Data-driven spectral analysis and nonparameteric predictions of climate dynamics (Mar 2020)
Laura Mansfield: Can we use machine learning to predict global patterns of climate change? (Mar 2020)
So Takao: Stochastic Advection by Lie Transport: A geometric framework for data-driven turbulence closure (Mar 2020)
Yasemin Bekiroglu: Towards Robust and Goal-oriented Robotic Grasping and Manipulation (Feb 2020)
Keshi He: Toward AI-powered Robots Served for IndustrialProductions and Healthcare (Feb 2020)
Vincent Adam: Variational inference for Gaussian Process models: multi-GP regression and time-series models (Feb 2020)
Rituraj Kaushik: Data-efficient adaptation in robotics using priors from the simulator (Feb 2020)
Rika Antonova: Adaptive Kernels and Priors for Data-efficient Bayesian Optimization (Feb 2020)
Vincent Adam: Doubly Sparse Variational Gaussian Processes (Oct 2019)
Samuel Kaski: Probabilistic Modelling with Experts (Oct 2019)
Christian Walder: New Tricks for Estimating Gradients of Expectations (Sep 2019)
Emtiyaz Khan: Learning-Algorithms from Bayesian Principle (Sep 2019)
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