Marc Peter Deisenroth

Orcid: 0000-0003-1503-680X

Affiliations:
  • University College London, UK
  • Imperial College London, Department of Computing (former)
  • TU Darmstadt, Department of Computer Science (former)


According to our database1, Marc Peter Deisenroth authored at least 130 papers between 2006 and 2024.

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Bibliography

2024
One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation.
CoRR, 2024

Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction.
CoRR, 2024

Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling.
CoRR, 2024

Valid Error Bars for Neural Weather Models using Conformal Prediction.
CoRR, 2024

RecMoDiffuse: Recurrent Flow Diffusion for Human Motion Generation.
CoRR, 2024

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks.
CoRR, 2024

Scalable Data Assimilation with Message Passing.
CoRR, 2024

Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems.
CoRR, 2024

Gaussian Processes on Cellular Complexes.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Unifying Variational Framework for Gaussian Process Motion Planning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Grasp Transfer Based on Self-Aligning Implicit Representations of Local Surfaces.
IEEE Robotics Autom. Lett., October, 2023

Faster Training of Neural ODEs Using Gauß-Legendre Quadrature.
Trans. Mach. Learn. Res., 2023

Plasma Surrogate Modelling using Fourier Neural Operators.
CoRR, 2023

On Combining Expert Demonstrations in Imitation Learning via Optimal Transport.
CoRR, 2023

Implicit regularisation in stochastic gradient descent: from single-objective to two-player games.
CoRR, 2023

Investigating the Edge of Stability Phenomenon in Reinforcement Learning.
CoRR, 2023

Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions.
CoRR, 2023

Queer In AI: A Case Study in Community-Led Participatory AI.
CoRR, 2023

Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes.
CoRR, 2023

Thin and deep Gaussian processes.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural Field Movement Primitives for Joint Modelling of Scenes and Motions.
IROS, 2023

Sliding Touch-Based Exploration for Modeling Unknown Object Shape with Multi-Fingered Hands.
IROS, 2023

Optimal Transport for Offline Imitation Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023


Understanding Deep Generative Models with Generalized Empirical Likelihoods.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Safe Trajectory Sampling in Model-Based Reinforcement Learning.
Proceedings of the 19th IEEE International Conference on Automation Science and Engineering, 2023

Actually Sparse Variational Gaussian Processes.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation.
Trans. Mach. Learn. Res., 2022

The Graph Cut Kernel for Ranked Data.
Trans. Mach. Learn. Res., 2022

Enhanced GPIS Learning Based on Local and Global Focus Areas.
IEEE Robotics Autom. Lett., 2022

Cauchy-Schwarz Regularized Autoencoder.
J. Mach. Learn. Res., 2022

One-Shot Transfer of Affordance Regions? AffCorrs!
Proceedings of the Conference on Robot Learning, 2022

Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation.
Proceedings of the 18th IEEE International Conference on Automation Science and Engineering, 2022

2021
Pathwise Conditioning of Gaussian Processes.
J. Mach. Learn. Res., 2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels.
CoRR, 2021

Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds.
CoRR, 2021

Learning to Transfer: A Foliated Theory.
CoRR, 2021

Submodular Kernels for Efficient Rankings.
CoRR, 2021

GPflux: A Library for Deep Gaussian Processes.
CoRR, 2021

Sliced Multi-Marginal Optimal Transport.
CoRR, 2021

Healing Products of Gaussian Processes.
CoRR, 2021

Design of Dynamic Experiments for Black-Box Model Discrimination.
CoRR, 2021

Copula Flows for Synthetic Data Generation.
CoRR, 2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Contact Dynamics using Physically Structured Neural Networks.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Aligning Time Series on Incomparable Spaces.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Matérn Gaussian Processes on Graphs.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
High-dimensional Bayesian optimization with projections using quantile Gaussian processes.
Optim. Lett., 2020

High-dimensional Bayesian optimization using low-dimensional feature spaces.
Mach. Learn., 2020

GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability.
CoRR, 2020

A Foliated View of Transfer Learning.
CoRR, 2020

Estimating Barycenters of Measures in High Dimensions.
CoRR, 2020

Probabilistic Active Meta-Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Matérn Gaussian Processes on Riemannian Manifolds.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficiently sampling functions from Gaussian process posteriors.
Proceedings of the 37th International Conference on Machine Learning, 2020

Stochastic Differential Equations with Variational Wishart Diffusions.
Proceedings of the 37th International Conference on Machine Learning, 2020

Healing Products of Gaussian Process Experts.
Proceedings of the 37th International Conference on Machine Learning, 2020

Variational Integrator Networks for Physically Structured Embeddings.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Bayesian Multiobjective Optimisation With Mixed Analytical and Black-Box Functions: Application to Tissue Engineering.
IEEE Trans. Biomed. Eng., 2019

Variational Integrator Networks for Physically Meaningful Embeddings.
CoRR, 2019

Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms.
CoRR, 2019

High-Dimensional Bayesian Optimization with Manifold Gaussian Processes.
CoRR, 2019

GPdoemd: A Python package for design of experiments for model discrimination.
Comput. Chem. Eng., 2019

Deep Gaussian Processes with Importance-Weighted Variational Inference.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Meta Reinforcement Learning with Latent Variable Gaussian Processes.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Maximizing acquisition functions for Bayesian optimization.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Orthogonally Decoupled Variational Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Gaussian Process Conditional Density Estimation.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches.
Proceedings of the 35th International Conference on Machine Learning, 2018

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Gaussian Process Domain Experts for Modeling of Facial Affect.
IEEE Trans. Image Process., 2017

Deep Reinforcement Learning: A Brief Survey.
IEEE Signal Process. Mag., 2017

The reparameterization trick for acquisition functions.
CoRR, 2017

A Brief Survey of Deep Reinforcement Learning.
CoRR, 2017

Customer Life Time Value Prediction Using Embeddings.
CoRR, 2017

Neural Embeddings of Graphs in Hyperbolic Space.
CoRR, 2017

Model-based contextual policy search for data-efficient generalization of robot skills.
Artif. Intell., 2017

Probabilistic Inference of Twitter Users' Age Based on What They Follow.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Doubly Stochastic Variational Inference for Deep Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Identification of Gaussian Process State Space Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Customer Lifetime Value Prediction Using Embeddings.
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

2016
Real-Time Association Mining in Large Social Networks.
CoRR, 2016

Detecting the Age of Twitter Users.
CoRR, 2016

Bayesian optimization for learning gaits under uncertainty - An experimental comparison on a dynamic bipedal walker.
Ann. Math. Artif. Intell., 2016

Social and Affective Robotics Tutorial.
Proceedings of the 2016 ACM Conference on Multimedia Conference, 2016

Manifold Gaussian Processes for regression.
Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs.
Proceedings of the 24th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2016

Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016

Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units.
Proceedings of the Computer Vision - ACCV 2016, 2016

2015
Gaussian Processes for Data-Efficient Learning in Robotics and Control.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

From Pixels to Torques: Policy Learning with Deep Dynamical Models.
CoRR, 2015

Bayesian Optimization with Dimension Scheduling: Application to Biological Systems.
CoRR, 2015

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models.
CoRR, 2015

Learning inverse dynamics models with contacts.
Proceedings of the IEEE International Conference on Robotics and Automation, 2015

Distributed Gaussian Processes.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Learning torque control in presence of contacts using tactile sensing from robot skin.
Proceedings of the 15th IEEE-RAS International Conference on Humanoid Robots, 2015

2014
Learning deep dynamical models from image pixels.
CoRR, 2014

Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression.
CoRR, 2014

Bayesian Gait Optimization for Bipedal Locomotion.
Proceedings of the Learning and Intelligent Optimization, 2014

Multi-task policy search for robotics.
Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

An experimental comparison of Bayesian optimization for bipedal locomotion.
Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

Policy search for learning robot control using sparse data.
Proceedings of the 2014 IEEE International Conference on Robotics and Automation, 2014

Multi-modal filtering for non-linear estimation.
Proceedings of the IEEE International Conference on Acoustics, 2014

Analytic Long-Term Forecasting with Periodic Gaussian Processes.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Probabilistic movement modeling for intention inference in human-robot interaction.
Int. J. Robotics Res., 2013

A Survey on Policy Search for Robotics.
Found. Trends Robotics, 2013

Multi-Task Policy Search.
CoRR, 2013

Probabilistic model-based imitation learning.
Adapt. Behav., 2013

Feedback error learning for rhythmic motor primitives.
Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

Model-based imitation learning by probabilistic trajectory matching.
Proceedings of the 2013 IEEE International Conference on Robotics and Automation, 2013

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

2012
Robust Filtering and Smoothing with Gaussian Processes.
IEEE Trans. Autom. Control., 2012

Probabilistic Modeling of Human Movements for Intention Inference.
Proceedings of the Robotics: Science and Systems VIII, 2012

Expectation Propagation in Gaussian Process Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Toward fast policy search for learning legged locomotion.
Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

Learning Deep Belief Networks from Non-stationary Streams.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2012, 2012

Preface.
Proceedings of the Tenth European Workshop on Reinforcement Learning, 2012

2011
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning.
Proceedings of the Robotics: Science and Systems VII, 2011

Gambit: An autonomous chess-playing robotic system.
Proceedings of the IEEE International Conference on Robotics and Automation, 2011

PILCO: A Model-Based and Data-Efficient Approach to Policy Search.
Proceedings of the 28th International Conference on Machine Learning, 2011

A general perspective on Gaussian filtering and smoothing: Explaining current and deriving new algorithms.
Proceedings of the American Control Conference, 2011

2010
Efficient reinforcement learning using Gaussian processes.
PhD thesis, 2010

State-Space Inference and Learning with Gaussian Processes.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

A Probabilistic Perspective on Gaussian Filtering and Smoothing
CoRR, 2010

2009
Gaussian process dynamic programming.
Neurocomputing, 2009

Analytic moment-based Gaussian process filtering.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Probabilistic Inference for Fast Learning in Control.
Proceedings of the Recent Advances in Reinforcement Learning, 8th European Workshop, 2008

Model-Based Reinforcement Learning with Continuous States and Actions.
Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

Approximate dynamic programming with Gaussian processes.
Proceedings of the American Control Conference, 2008

2006
Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle.
Proceedings of the 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2006


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