Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle. - Marc Peter Deisenroth; Toshiyuki Ohtsuka; Florian Weissel; Dietrich Brunn; Uwe D. Hanebeck (MFI, 2006)

Approximate dynamic programming with Gaussian processes. - Marc Peter Deisenroth; Jan Peters 001; Carl E. Rasmussen (ACC, 2008)

Model-Based Reinforcement Learning with Continuous States and Actions. - Marc Peter Deisenroth; Carl Edward Rasmussen; Jan Peters 001 (ESANN, 2008)

Probabilistic Inference for Fast Learning in Control. - Carl Edward Rasmussen; Marc Peter Deisenroth (EWRL, 2008)

Analytic moment-based Gaussian process filtering. - Marc Peter Deisenroth; Marco F. Huber; Uwe D. Hanebeck (ICML, 2009)

Gaussian process dynamic programming. - Marc Peter Deisenroth; Carl Edward Rasmussen; Jan Peters 001 (Neurocomputing, 2009)

A Probabilistic Perspective on Gaussian Filtering and Smoothing - Marc Peter Deisenroth; Henrik Ohlsson (CoRR, 2010)

State-Space Inference and Learning with Gaussian Processes. - Ryan D. Turner; Marc Peter Deisenroth; Carl Edward Rasmussen (AISTATS, 2010)

Efficient reinforcement learning using Gaussian processes. - Marc Peter Deisenroth (2010)

A general perspective on Gaussian filtering and smoothing: Explaining current and deriving new algorithms. - Marc Peter Deisenroth; Henrik Ohlsson (ACC, 2011)

PILCO: A Model-Based and Data-Efficient Approach to Policy Search. - Marc Peter Deisenroth; Carl Edward Rasmussen (ICML, 2011)

Gambit: An autonomous chess-playing robotic system. - Cynthia Matuszek; Brian Mayton; Roberto Aimi; Marc Peter Deisenroth; Liefeng Bo; Robert Chu; Mike Kung; Louis LeGrand; Joshua R. Smith 001; Dieter Fox (ICRA, 2011)

Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning. - Marc Peter Deisenroth; Carl Edward Rasmussen; Dieter Fox (Robotics: Science and Systems, 2011)

Preface. - Marc Peter Deisenroth; Csaba Szepesvári; Jan Peters 001 (EWRL, 2012)

Learning Deep Belief Networks from Non-stationary Streams. - Roberto Calandra; Tapani Raiko; Marc Peter Deisenroth; Federico Montesino-Pouzols (ICANN (2), 2012)

Toward fast policy search for learning legged locomotion. - Marc Peter Deisenroth; Roberto Calandra; André Seyfarth; Jan Peters 001 (IROS, 2012)

Expectation Propagation in Gaussian Process Dynamical Systems. - Marc Peter Deisenroth; Shakir Mohamed (NIPS, 2012)

Probabilistic Modeling of Human Movements for Intention Inference. - Zhikun Wang; Marc Peter Deisenroth; Heni Ben Amor; David Vogt; Bernhard Schölkopf; Jan Peters 001 (Robotics: Science and Systems, 2012)

Robust Filtering and Smoothing with Gaussian Processes. - Marc Peter Deisenroth; Ryan D. Turner; Marco F. Huber; Uwe D. Hanebeck; Carl Edward Rasmussen (IEEE Trans. Autom. Control., 2012)

Data-Efficient Generalization of Robot Skills with Contextual Policy Search. - Andras Gabor Kupcsik; Marc Peter Deisenroth; Jan Peters 001; Gerhard Neumann (AAAI, 2013)

Model-based imitation learning by probabilistic trajectory matching. - Peter Englert; Alexandros Paraschos; Jan Peters 001; Marc Peter Deisenroth (ICRA, 2013)

Feedback error learning for rhythmic motor primitives. - Nakul Gopalan; Marc Peter Deisenroth; Jan Peters 001 (ICRA, 2013)

Probabilistic model-based imitation learning. - Peter Englert; Alexandros Paraschos; Marc Peter Deisenroth; Jan Peters 001 (Adapt. Behav., 2013)

Multi-Task Policy Search. - Marc Peter Deisenroth; Peter Englert; Jan Peters 001; Dieter Fox (CoRR, 2013)

A Survey on Policy Search for Robotics. - Marc Peter Deisenroth; Gerhard Neumann; Jan Peters 001 (Found. Trends Robotics, 2013)

Probabilistic movement modeling for intention inference in human-robot interaction. - Zhikun Wang; Katharina Mülling; Marc Peter Deisenroth; Heni Ben Amor; David Vogt; Bernhard Schölkopf; Jan Peters 001 (Int. J. Robotics Res., 2013)

Analytic Long-Term Forecasting with Periodic Gaussian Processes. - Nooshin HajiGhassemi; Marc Peter Deisenroth (AISTATS, 2014)

Multi-modal filtering for non-linear estimation. - Sanket Kamthe; Jan Peters 001; Marc Peter Deisenroth (ICASSP, 2014)

Policy search for learning robot control using sparse data. - Bastian Bischoff; Duy Nguyen-Tuong; Herke van Hoof; Andrew McHutchon; Carl E. Rasmussen; Alois C. Knoll; Jan Peters 001; Marc Peter Deisenroth (ICRA, 2014)

An experimental comparison of Bayesian optimization for bipedal locomotion. - Roberto Calandra; André Seyfarth; Jan Peters 001; Marc Peter Deisenroth (ICRA, 2014)

Multi-task policy search for robotics. - Marc Peter Deisenroth; Peter Englert; Jan Peters 001; Dieter Fox (ICRA, 2014)

Bayesian Gait Optimization for Bipedal Locomotion. - Roberto Calandra; Nakul Gopalan; André Seyfarth; Jan Peters 001; Marc Peter Deisenroth (LION, 2014)

Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression. - Jun Wei Ng; Marc Peter Deisenroth (CoRR, 2014)

Learning deep dynamical models from image pixels. - Niklas Wahlström; Thomas B. Schön; Marc Peter Deisenroth (CoRR, 2014)

Learning torque control in presence of contacts using tactile sensing from robot skin. - Roberto Calandra; Serena Ivaldi; Marc Peter Deisenroth; Jan Peters 001 (Humanoids, 2015)

Distributed Gaussian Processes. - Marc Peter Deisenroth; Jun Wei Ng (ICML, 2015)

Learning inverse dynamics models with contacts. - Roberto Calandra; Serena Ivaldi; Marc Peter Deisenroth; Elmar Rueckert; Jan Peters 001 (ICRA, 2015)

Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. - John-Alexander M. Assael; Niklas Wahlström; Thomas B. Schön; Marc Peter Deisenroth (CoRR, 2015)

Bayesian Optimization with Dimension Scheduling: Application to Biological Systems. - Doniyor Ulmasov; Caroline Baroukh; Benoît Chachuat; Marc Peter Deisenroth; Ruth Misener (CoRR, 2015)

From Pixels to Torques: Policy Learning with Deep Dynamical Models. - Niklas Wahlström; Thomas B. Schön; Marc Peter Deisenroth (CoRR, 2015)

Gaussian Processes for Data-Efficient Learning in Robotics and Control. - Marc Peter Deisenroth; Dieter Fox; Carl Edward Rasmussen (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units. - Stefanos Eleftheriadis; Ognjen Rudovic; Marc Peter Deisenroth; Maja Pantic (ACCV (2), 2016)

Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis. - Stefanos Eleftheriadis; Ognjen Rudovic; Marc Peter Deisenroth; Maja Pantic (CVPR Workshops, 2016)

Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs. - Maciej Kurek; Marc Peter Deisenroth; Wayne Luk; Timothy John Todman (FCCM, 2016)

Manifold Gaussian Processes for regression. - Roberto Calandra; Jan Peters 001; Carl Edward Rasmussen; Marc Peter Deisenroth (IJCNN, 2016)

Social and Affective Robotics Tutorial. - Maja Pantic; Vanessa Evers; Marc Peter Deisenroth; Luis Merino; Björn W. Schuller (ACM Multimedia, 2016)

Bayesian optimization for learning gaits under uncertainty - An experimental comparison on a dynamic bipedal walker. - Roberto Calandra; André Seyfarth; Jan Peters 001; Marc Peter Deisenroth (Ann. Math. Artif. Intell., 2016)

Detecting the Age of Twitter Users. - Benjamin Paul Chamberlain; Clive Humby; Marc Peter Deisenroth (CoRR, 2016)

Real-Time Association Mining in Large Social Networks. - Benjamin Paul Chamberlain; Josh Levy-Kramer; Clive Humby; Marc Peter Deisenroth (CoRR, 2016)

Customer Lifetime Value Prediction Using Embeddings. - Benjamin Paul Chamberlain; Ângelo Cardoso; C. H. Bryan Liu; Roberto Pagliari; Marc Peter Deisenroth (KDD, 2017)

Identification of Gaussian Process State Space Models. - Stefanos Eleftheriadis; Tom Nicholson; Marc Peter Deisenroth; James Hensman (NIPS, 2017)

Doubly Stochastic Variational Inference for Deep Gaussian Processes. - Hugh Salimbeni; Marc Peter Deisenroth (NIPS, 2017)

Probabilistic Inference of Twitter Users' Age Based on What They Follow. - Benjamin Paul Chamberlain; Clive Humby; Marc Peter Deisenroth (ECML/PKDD (3), 2017)

Model-based contextual policy search for data-efficient generalization of robot skills. - Andras Gabor Kupcsik; Marc Peter Deisenroth; Jan Peters 001; Ai Poh Loh; Prahlad Vadakkepat; Gerhard Neumann (Artif. Intell., 2017)

Neural Embeddings of Graphs in Hyperbolic Space. - Benjamin Paul Chamberlain; James R. Clough; Marc Peter Deisenroth (CoRR, 2017)

Customer Life Time Value Prediction Using Embeddings. - Benjamin Paul Chamberlain; Ângelo Cardoso; C. H. Bryan Liu; Roberto Pagliari; Marc Peter Deisenroth (CoRR, 2017)

A Brief Survey of Deep Reinforcement Learning. - Kai Arulkumaran; Marc Peter Deisenroth; Miles Brundage; Anil Anthony Bharath (CoRR, 2017)

The reparameterization trick for acquisition functions. - James T. Wilson; Riccardo Moriconi; Frank Hutter; Marc Peter Deisenroth (CoRR, 2017)

Deep Reinforcement Learning: A Brief Survey. - Kai Arulkumaran; Marc Peter Deisenroth; Miles Brundage; Anil Anthony Bharath (IEEE Signal Process. Mag., 2017)

Gaussian Process Domain Experts for Modeling of Facial Affect. - Stefanos Eleftheriadis; Ognjen Rudovic; Marc Peter Deisenroth; Maja Pantic (IEEE Trans. Image Process., 2017)

Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. - Sanket Kamthe; Marc Peter Deisenroth (AISTATS, 2018)

Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches. - Simon Olofsson; Marc Peter Deisenroth; Ruth Misener (ICML, 2018)

Gaussian Process Conditional Density Estimation. - Vincent Dutordoir; Hugh Salimbeni; James Hensman; Marc Peter Deisenroth (NeurIPS, 2018)

Orthogonally Decoupled Variational Gaussian Processes. - Hugh Salimbeni; Ching-An Cheng; Byron Boots; Marc Peter Deisenroth (NeurIPS, 2018)

Maximizing acquisition functions for Bayesian optimization. - James T. Wilson; Frank Hutter; Marc Peter Deisenroth (NeurIPS, 2018)

Meta Reinforcement Learning with Latent Variable Gaussian Processes. - Steindór Sæmundsson; Katja Hofmann; Marc Peter Deisenroth (UAI, 2018)

Deep Gaussian Processes with Importance-Weighted Variational Inference. - Hugh Salimbeni; Vincent Dutordoir; James Hensman; Marc Peter Deisenroth (ICML, 2019)

GPdoemd: A Python package for design of experiments for model discrimination. - Simon Olofsson; Lukas Hebing; Sebastian Niedenführ; Marc Peter Deisenroth; Ruth Misener (Comput. Chem. Eng., 2019)

High-Dimensional Bayesian Optimization with Manifold Gaussian Processes. - Riccardo Moriconi; K. S. Sesh Kumar; Marc Peter Deisenroth (CoRR, 2019)

Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms. - K. S. Sesh Kumar; Marc Peter Deisenroth (CoRR, 2019)

Variational Integrator Networks for Physically Meaningful Embeddings. - Steindór Sæmundsson; Alexander Terenin; Katja Hofmann; Marc Peter Deisenroth (CoRR, 2019)

Bayesian Multiobjective Optimisation With Mixed Analytical and Black-Box Functions: Application to Tissue Engineering. - Simon Olofsson; Mohammad Mehrian; Roberto Calandra; Liesbet Geris; Marc Peter Deisenroth; Ruth Misener (IEEE Trans. Biomed. Eng., 2019)

Variational Integrator Networks for Physically Structured Embeddings. - Steindór Sæmundsson; Alexander Terenin; Katja Hofmann; Marc Peter Deisenroth (AISTATS, 2020)

Healing Products of Gaussian Process Experts. - Samuel Cohen; Rendani Mbuvha; Tshilidzi Marwala; Marc Peter Deisenroth (ICML, 2020)

Stochastic Differential Equations with Variational Wishart Diffusions. - Martin Jørgensen; Marc Peter Deisenroth; Hugh Salimbeni (ICML, 2020)

Efficiently sampling functions from Gaussian process posteriors. - James T. Wilson; Viacheslav Borovitskiy; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth (ICML, 2020)

Matérn Gaussian Processes on Riemannian Manifolds. - Viacheslav Borovitskiy; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth (NeurIPS, 2020)

Probabilistic Active Meta-Learning. - Jean Kaddour; Steindór Sæmundsson; Marc Peter Deisenroth (NeurIPS, 2020)

Estimating Barycenters of Measures in High Dimensions. - Samuel Cohen; Michael Arbel; Marc Peter Deisenroth (CoRR, 2020)

A Foliated View of Transfer Learning. - Janith C. Petangoda; Nick A. M. Monk; Marc Peter Deisenroth (CoRR, 2020)

GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability. - Daniel Lengyel; Janith C. Petangoda; Isak Falk; Kate Highnam; Michalis Lazarou; Arinbjörn Kolbeinsson; Marc Peter Deisenroth; Nicholas R. Jennings (CoRR, 2020)

High-dimensional Bayesian optimization using low-dimensional feature spaces. - Riccardo Moriconi; Marc Peter Deisenroth; K. S. Sesh Kumar (Mach. Learn., 2020)

High-dimensional Bayesian optimization with projections using quantile Gaussian processes. - Riccardo Moriconi; K. S. Sesh Kumar; Marc Peter Deisenroth (Optim. Lett., 2020)

Matérn Gaussian Processes on Graphs. - Viacheslav Borovitskiy; Iskander Azangulov; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth; Nicolas Durrande (AISTATS, 2021)

Aligning Time Series on Incomparable Spaces. - Samuel Cohen; Giulia Luise; Alexander Terenin; Brandon Amos; Marc Peter Deisenroth (AISTATS, 2021)

Learning Contact Dynamics using Physically Structured Neural Networks. - Andreas Hochlehnert; Alexander Terenin; Steindór Sæmundsson; Marc Peter Deisenroth (AISTATS, 2021)

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. - Michael J. Hutchinson; Alexander Terenin; Viacheslav Borovitskiy; So Takao; Yee Whye Teh; Marc Peter Deisenroth (NeurIPS, 2021)

Copula Flows for Synthetic Data Generation. - Sanket Kamthe; Samuel Assefa; Marc Peter Deisenroth (CoRR, 2021)

Design of Dynamic Experiments for Black-Box Model Discrimination. - Simon Olofsson; Eduardo S. Schultz; Adel Mhamdi; Alexander Mitsos; Marc Peter Deisenroth; Ruth Misener (CoRR, 2021)

Healing Products of Gaussian Processes. - Samuel Cohen; Rendani Mbuvha; Tshilidzi Marwala; Marc Peter Deisenroth (CoRR, 2021)

Sliced Multi-Marginal Optimal Transport. - Samuel Cohen; K. S. Sesh Kumar; Marc Peter Deisenroth (CoRR, 2021)

GPflux: A Library for Deep Gaussian Processes. - Vincent Dutordoir; Hugh Salimbeni; Eric Hambro; John McLeod; Felix Leibfried; Artem Artemev; Mark van der Wilk; James Hensman; Marc Peter Deisenroth; S. T. John (CoRR, 2021)

Submodular Kernels for Efficient Rankings. - Michelangelo Conserva; Marc Peter Deisenroth; K. S. Sesh Kumar (CoRR, 2021)

Learning to Transfer: A Foliated Theory. - Janith C. Petangoda; Marc Peter Deisenroth; Nicholas A. M. Monk (CoRR, 2021)

Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds. - Vu Nguyen; Marc Peter Deisenroth; Michael A. Osborne (CoRR, 2021)

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels. - Michael J. Hutchinson; Alexander Terenin; Viacheslav Borovitskiy; So Takao; Yee Whye Teh; Marc Peter Deisenroth (CoRR, 2021)

Pathwise Conditioning of Gaussian Processes. - James T. Wilson; Viacheslav Borovitskiy; Alexander Terenin; Peter Mostowsky; Marc Peter Deisenroth (J. Mach. Learn. Res., 2021)

Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation. - Hadi Hajieghrary; Marc Peter Deisenroth; Yasemin Bekiroglu (CASE, 2022)

One-Shot Transfer of Affordance Regions? AffCorrs! - Denis Hadjivelichkov; Sicelukwanda Zwane; Lourdes Agapito; Marc Peter Deisenroth; Dimitrios Kanoulas (CoRL, 2022)

Cauchy-Schwarz Regularized Autoencoder. - Linh Tran; Maja Pantic; Marc Peter Deisenroth (J. Mach. Learn. Res., 2022)

Enhanced GPIS Learning Based on Local and Global Focus Areas. - Zuka Murvanidze; Marc Peter Deisenroth; Yasemin Bekiroglu (IEEE Robotics Autom. Lett., 2022)

The Graph Cut Kernel for Ranked Data. - Michelangelo Conserva; Marc Peter Deisenroth; K. S. Sesh Kumar (Trans. Mach. Learn. Res., 2022)

Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation. - Sanket Kamthe; So Takao; Shakir Mohamed; Marc Peter Deisenroth (Trans. Mach. Learn. Res., 2022)

Actually Sparse Variational Gaussian Processes. - Harry Jake Cunningham; Daniel Augusto de Souza; So Takao; Mark van der Wilk; Marc Peter Deisenroth (AISTATS, 2023)

Safe Trajectory Sampling in Model-Based Reinforcement Learning. - Sicelukwanda Zwane; Denis Hadjivelichkov; Yicheng Luo; Yasemin Bekiroglu; Dimitrios Kanoulas; Marc Peter Deisenroth (CASE, 2023)

Understanding Deep Generative Models with Generalized Empirical Likelihoods. - Suman V. Ravuri; Mélanie Rey; Shakir Mohamed; Marc Peter Deisenroth (CVPR, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - Organizers Of QueerInAI; Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; et al. (FAccT, 2023)

Optimal Transport for Offline Imitation Learning. - Yicheng Luo; Zhengyao Jiang; Samuel Cohen; Edward Grefenstette; Marc Peter Deisenroth (ICLR, 2023)

Sliding Touch-Based Exploration for Modeling Unknown Object Shape with Multi-Fingered Hands. - Yiting Chen; Ahmet Ercan Tekden; Marc Peter Deisenroth; Yasemin Bekiroglu (IROS, 2023)

Neural Field Movement Primitives for Joint Modelling of Scenes and Motions. - Ahmet Ercan Tekden; Marc Peter Deisenroth; Yasemin Bekiroglu (IROS, 2023)

Thin and deep Gaussian processes. - Daniel Augusto de Souza; Alexander Nikitin; St John; Magnus Ross; Mauricio A. Álvarez; Marc Peter Deisenroth; João Paulo Pordeus Gomes; Diego Mesquita; César Lincoln C. Mattos (NeurIPS, 2023)

Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes. - Sean Nassimiha; Peter Dudfield; Jack Kelly; Marc Peter Deisenroth; So Takao (CoRR, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; Jaidev Shriram; et al. (CoRR, 2023)

Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions. - Yicheng Luo; Jackie Kay; Edward Grefenstette; Marc Peter Deisenroth (CoRR, 2023)

Investigating the Edge of Stability Phenomenon in Reinforcement Learning. - Rares Iordan; Marc Peter Deisenroth; Mihaela Rosca (CoRR, 2023)

Implicit regularisation in stochastic gradient descent: from single-objective to two-player games. - Mihaela Rosca; Marc Peter Deisenroth (CoRR, 2023)

On Combining Expert Demonstrations in Imitation Learning via Optimal Transport. - Ilana Sebag; Samuel Cohen; Marc Peter Deisenroth (CoRR, 2023)

Plasma Surrogate Modelling using Fourier Neural Operators. - Vignesh Gopakumar; Stanislas Pamela; Lorenzo Zanisi; Zongyi Li; Ander Gray; Daniel Brennand; Nitesh Bhatia; Gregory Stathopoulos; Matt J. Kusner; Marc Peter Deisenroth; Anima Anandkumar; JOREK Team; et al. (CoRR, 2023)

Faster Training of Neural ODEs Using Gauß-Legendre Quadrature. - Alexander Luke Ian Norcliffe; Marc Peter Deisenroth (Trans. Mach. Learn. Res., 2023)

Grasp Transfer Based on Self-Aligning Implicit Representations of Local Surfaces. - Ahmet Ercan Tekden; Marc Peter Deisenroth; Yasemin Bekiroglu (IEEE Robotics Autom. Lett., 2023)

A Unifying Variational Framework for Gaussian Process Motion Planning. - Lucas Cosier; Rares Iordan; Sicelukwanda N. T. Zwane; Giovanni Franzese; James T. Wilson; Marc Peter Deisenroth; Alexander Terenin; Yasemin Bekiroglu (AISTATS, 2024)

Gaussian Processes on Cellular Complexes. - Mathieu Alain; So Takao; Brooks Paige; Marc Peter Deisenroth (ICML, 2024)

Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials. - Sicelukwanda Zwane; Daniel G. Cheney; Curtis C. Johnson; Yicheng Luo; Yasemin Bekiroglu; Marc D. Killpack; Marc Peter Deisenroth (IROS, 2024)

Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. - Jake Cunningham; Giorgio Giannone; Mingtian Zhang; Marc Peter Deisenroth (NeurIPS, 2024)

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. - Joel Oskarsson; Tomas Landelius; Marc Peter Deisenroth; Fredrik Lindsten (NeurIPS, 2024)

Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems. - Rafael Anderka; Marc Peter Deisenroth; So Takao (UAI, 2024)

Scalable Data Assimilation with Message Passing. - Oscar Key; So Takao; Daniel Giles; Marc Peter Deisenroth (CoRR, 2024)

RecMoDiffuse: Recurrent Flow Diffusion for Human Motion Generation. - Mirgahney Mohamed; Harry Jake Cunningham; Marc Peter Deisenroth; Lourdes Agapito (CoRR, 2024)

Valid Error Bars for Neural Weather Models using Conformal Prediction. - Vignesh Gopakumar; Joel Oskarrson; Ander Gray; Lorenzo Zanisi; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2024)

Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction. - Vignesh Gopakumar; Ander Gray; Joel Oskarsson; Lorenzo Zanisi; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2024)

One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation. - Fabian Paischer; Lukas Hauzenberger; Thomas Schmied; Benedikt Alkin; Marc Peter Deisenroth; Sepp Hochreiter (CoRR, 2024)

Calibrated Physics-Informed Uncertainty Quantification. - Vignesh Gopakumar; Ander Gray; Lorenzo Zanisi; Timothy Nunn; Stanislas Pamela; Daniel Giles; Matt J. Kusner; Marc Peter Deisenroth (CoRR, 2025)