Marc P. Deisenroth

Publications

Plasma Surrogate Modelling using Fourier Neural Operators (2024)
A Unifying Variational Framework for Gaussian Process Motion Planning (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)
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)
Enhanced GPIS Learning Based on Local and Global Focus Areas (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)
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 Independent Projected Kernels (2021)
Pathwise Conditioning of Gaussian Processes (2021)
GPflux: A Library for Deep Gaussian Processes (2021)
High-Dimensional Bayesian Optimization Using Low-Dimensional Feature Spaces (2019)
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms (2019)
Deep Gaussian Processes with Importance-Weighted Variational Inference (2019)
Accelerating the BSM Interpretation of LHC Data with Machine Learning (2019)
Variational Integrator Networks for Physically Meaningful Embeddings (2019)
Maximizing Acquisition Functions for Bayesian Optimization (2018)
Orthogonally Decoupled Variational Gaussian Processes (2018)
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control (2018)
Bayesian Multi-Objective Optimisation with Mixed Analytical and Black-Box Functions: Application to Tissue Engineering (2018)
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches (2018)
Gaussian Process Conditional Density Estimation (2018)
Real-Time Community Detection in Full Social Networks on a Laptop (2018)
Doubly Stochastic Variational Inference for Deep Gaussian Processes (2017)
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)
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 Movement Modeling for Intention-based Decision Making (2013)
Expectation Propagation in Gaussian Process Dynamical Systems (2012)
Autonomous Planning and Control with Bayesian Nonparametric Models (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)