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ORCIDMarc Peter Deisenroth
Person information
- affiliation: University College London, UK
- affiliation (former): Imperial College London, Department of Computing
- affiliation (former): TU Darmstadt, Department of Computer Science
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2020 – today
- 2025
[i79]Vignesh Gopakumar, Ander Gray, Lorenzo Zanisi, Timothy Nunn, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth:
Calibrated Physics-Informed Uncertainty Quantification. CoRR abs/2502.04406 (2025)- 2024
- [c69]Lucas Cosier, Rares Iordan, Sicelukwanda N. T. Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu:
A Unifying Variational Framework for Gaussian Process Motion Planning. AISTATS 2024: 1315-1323 - [c68]Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth:
Gaussian Processes on Cellular Complexes. ICML 2024 - [c67]Sicelukwanda Zwane, Daniel G. Cheney, Curtis C. Johnson, Yicheng Luo, Yasemin Bekiroglu, Marc D. Killpack, Marc Peter Deisenroth:
Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials. IROS 2024: 11388-11393 - [c66]Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth:
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. NeurIPS 2024 - [c65]Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten:
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. NeurIPS 2024 - [c64]Rafael Anderka, Marc Peter Deisenroth, So Takao:
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems. UAI 2024: 50-76 - [i78]Rafael Anderka, Marc Peter Deisenroth, So Takao:
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems. CoRR abs/2402.17036 (2024) - [i77]Oscar Key, So Takao, Daniel Giles, Marc Peter Deisenroth:
Scalable Data Assimilation with Message Passing. CoRR abs/2404.12968 (2024) - [i76]Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten:
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. CoRR abs/2406.04759 (2024) - [i75]Mirgahney Mohamed, Harry Jake Cunningham, Marc Peter Deisenroth, Lourdes Agapito:
RecMoDiffuse: Recurrent Flow Diffusion for Human Motion Generation. CoRR abs/2406.07169 (2024) - [i74]Vignesh Gopakumar, Joel Oskarrson, Ander Gray, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth:
Valid Error Bars for Neural Weather Models using Conformal Prediction. CoRR abs/2406.14483 (2024) - [i73]Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth:
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. CoRR abs/2408.09453 (2024) - [i72]Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt J. Kusner, Marc Peter Deisenroth:
Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction. CoRR abs/2408.09881 (2024) - [i71]Fabian Paischer, Lukas Hauzenberger, Thomas Schmied, Benedikt Alkin, Marc Peter Deisenroth, Sepp Hochreiter:
One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation. CoRR abs/2410.07170 (2024) - [i70]Sicelukwanda Zwane, Daniel G. Cheney, Curtis C. Johnson, Yicheng Luo, Yasemin Bekiroglu, Marc D. Killpack, Marc Peter Deisenroth:
Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials. CoRR abs/2411.07342 (2024) - 2023
- [j21]Ahmet Ercan Tekden
, Marc Peter Deisenroth, Yasemin Bekiroglu:
Grasp Transfer Based on Self-Aligning Implicit Representations of Local Surfaces. IEEE Robotics Autom. Lett. 8(10): 6315-6322 (2023) - [j20]Alexander Luke Ian Norcliffe, Marc Peter Deisenroth:
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature. Trans. Mach. Learn. Res. 2023 (2023) - [c63]Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth:
Actually Sparse Variational Gaussian Processes. AISTATS 2023: 10395-10408 - [c62]Sicelukwanda Zwane, Denis Hadjivelichkov, Yicheng Luo, Yasemin Bekiroglu, Dimitrios Kanoulas, Marc Peter Deisenroth:
Safe Trajectory Sampling in Model-Based Reinforcement Learning. CASE 2023: 1-6 - [c61]Suman V. Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth:
Understanding Deep Generative Models with Generalized Empirical Likelihoods. CVPR 2023: 24395-24405 - [c60]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, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark:
Queer In AI: A Case Study in Community-Led Participatory AI. FAccT 2023: 1882-1895 - [c59]Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth:
Optimal Transport for Offline Imitation Learning. ICLR 2023 - [c58]Ahmet Ercan Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu:
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions. IROS 2023: 3648-3655 - [c57]Yiting Chen, Ahmet Ercan Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu:
Sliding Touch-Based Exploration for Modeling Unknown Object Shape with Multi-Fingered Hands. IROS 2023: 8943-8950 - [c56]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:
Thin and deep Gaussian processes. NeurIPS 2023 - [i69]Sean Nassimiha, Peter Dudfield, Jack Kelly, Marc Peter Deisenroth, So Takao:
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes. CoRR abs/2302.00388 (2023) - [i68]Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth:
Optimal Transport for Offline Imitation Learning. CoRR abs/2303.13971 (2023) - [i67]Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, Pranav A, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark:
Queer In AI: A Case Study in Community-Led Participatory AI. CoRR abs/2303.16972 (2023) - [i66]Yicheng Luo, Jackie Kay, Edward Grefenstette, Marc Peter Deisenroth:
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions. CoRR abs/2303.17396 (2023) - [i65]Harry Jake Cunningham, Daniel Augusto de Souza, So Takao, Mark van der Wilk, Marc Peter Deisenroth:
Actually Sparse Variational Gaussian Processes. CoRR abs/2304.05091 (2023) - [i64]Suman V. Ravuri, Mélanie Rey, Shakir Mohamed, Marc Peter Deisenroth:
Understanding Deep Generative Models with Generalized Empirical Likelihoods. CoRR abs/2306.09780 (2023) - [i63]Rares Iordan, Marc Peter Deisenroth, Mihaela Rosca:
Investigating the Edge of Stability Phenomenon in Reinforcement Learning. CoRR abs/2307.04210 (2023) - [i62]Mihaela Rosca, Marc Peter Deisenroth:
Implicit regularisation in stochastic gradient descent: from single-objective to two-player games. CoRR abs/2307.05789 (2023) - [i61]Ilana Sebag, Samuel Cohen, Marc Peter Deisenroth:
On Combining Expert Demonstrations in Imitation Learning via Optimal Transport. CoRR abs/2307.10810 (2023) - [i60]Yiting Chen, Ahmet Ercan Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu:
Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-fingered Hands. CoRR abs/2308.00576 (2023) - [i59]Ahmet Ercan Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu:
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions. CoRR abs/2308.05040 (2023) - [i58]Ahmet Ercan Tekden, Marc Peter Deisenroth, Yasemin Bekiroglu:
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces. CoRR abs/2308.07807 (2023) - [i57]Alexander Norcliffe, Marc Peter Deisenroth:
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature. CoRR abs/2308.10644 (2023) - [i56]Lucas Cosier, Rares Iordan, Sicelukwanda Zwane, Giovanni Franzese, James T. Wilson, Marc Peter Deisenroth, Alexander Terenin, Yasemin Bekiroglu:
A Unifying Variational Framework for Gaussian Process Motion Planning. CoRR abs/2309.00854 (2023) - [i55]Daniel Augusto de Souza, Alexander Nikitin, St John, Magnus Ross, Mauricio A. Álvarez, Marc Peter Deisenroth, João P. P. Gomes, Diego Mesquita, César Lincoln C. Mattos:
Thin and Deep Gaussian Processes. CoRR abs/2310.11527 (2023) - [i54]Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth:
Gaussian Processes on Cellular Complexes. CoRR abs/2311.01198 (2023) - [i53]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, MAST Team:
Plasma Surrogate Modelling using Fourier Neural Operators. CoRR abs/2311.05967 (2023) - 2022
- [j19]Linh Tran, Maja Pantic, Marc Peter Deisenroth:
Cauchy-Schwarz Regularized Autoencoder. J. Mach. Learn. Res. 23: 115:1-115:37 (2022) - [j18]Zuka Murvanidze, Marc Peter Deisenroth, Yasemin Bekiroglu:
Enhanced GPIS Learning Based on Local and Global Focus Areas. IEEE Robotics Autom. Lett. 7(4): 11759-11766 (2022) - [j17]Michelangelo Conserva, Marc Peter Deisenroth, K. S. Sesh Kumar:
The Graph Cut Kernel for Ranked Data. Trans. Mach. Learn. Res. 2022 (2022) - [j16]Sanket Kamthe, So Takao, Shakir Mohamed, Marc Peter Deisenroth:
Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation. Trans. Mach. Learn. Res. 2022 (2022) - [c55]Hadi Hajieghrary, Marc Peter Deisenroth, Yasemin Bekiroglu:
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation. CASE 2022: 1009-1016 - [c54]Denis Hadjivelichkov, Sicelukwanda Zwane, Lourdes Agapito, Marc Peter Deisenroth, Dimitrios Kanoulas:
One-Shot Transfer of Affordance Regions? AffCorrs! CoRL 2022: 550-560 - [i52]Hadi Hajieghrary, Marc Peter Deisenroth, Yasemin Bekiroglu:
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation. CoRR abs/2207.04866 (2022) - [i51]Denis Hadjivelichkov, Sicelukwanda Zwane, Marc Peter Deisenroth, Lourdes Agapito, Dimitrios Kanoulas:
One-Shot Transfer of Affordance Regions? AffCorrs! CoRR abs/2209.07147 (2022) - 2021
- [j15]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Pathwise Conditioning of Gaussian Processes. J. Mach. Learn. Res. 22: 105:1-105:47 (2021) - [c53]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. AISTATS 2021: 1036-1044 - [c52]Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth:
Learning Contact Dynamics using Physically Structured Neural Networks. AISTATS 2021: 2152-2160 - [c51]Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande:
Matérn Gaussian Processes on Graphs. AISTATS 2021: 2593-2601 - [c50]Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth:
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels. NeurIPS 2021: 17160-17169 - [i50]Sanket Kamthe, Samuel Assefa, Marc Peter Deisenroth:
Copula Flows for Synthetic Data Generation. CoRR abs/2101.00598 (2021) - [i49]Linh Tran, Maja Pantic, Marc Peter Deisenroth:
Cauchy-Schwarz Regularized Autoencoder. CoRR abs/2101.02149 (2021) - [i48]Simon Olofsson, Eduardo S. Schultz, Adel Mhamdi, Alexander Mitsos, Marc Peter Deisenroth, Ruth Misener:
Design of Dynamic Experiments for Black-Box Model Discrimination. CoRR abs/2102.03782 (2021) - [i47]Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Peter Deisenroth:
Healing Products of Gaussian Processes. CoRR abs/2102.07106 (2021) - [i46]Samuel Cohen, K. S. Sesh Kumar, Marc Peter Deisenroth:
Sliced Multi-Marginal Optimal Transport. CoRR abs/2102.07115 (2021) - [i45]Andreas Hochlehnert, Alexander Terenin, Steindór Sæmundsson, Marc Peter Deisenroth:
Learning Contact Dynamics using Physically Structured Neural Networks. CoRR abs/2102.11206 (2021) - [i44]Vincent Dutordoir, Hugh Salimbeni, Eric Hambro, John McLeod, Felix Leibfried, Artem Artemev, Mark van der Wilk, James Hensman, Marc Peter Deisenroth, S. T. John:
GPflux: A Library for Deep Gaussian Processes. CoRR abs/2104.05674 (2021) - [i43]Michelangelo Conserva, Marc Peter Deisenroth, K. S. Sesh Kumar:
Submodular Kernels for Efficient Rankings. CoRR abs/2105.12356 (2021) - [i42]Janith C. Petangoda, Marc Peter Deisenroth, Nicholas A. M. Monk:
Learning to Transfer: A Foliated Theory. CoRR abs/2107.10763 (2021) - [i41]Vu Nguyen, Marc Peter Deisenroth, Michael A. Osborne:
Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds. CoRR abs/2110.12087 (2021) - [i40]Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth:
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels. CoRR abs/2110.14423 (2021) - 2020
- [j14]Riccardo Moriconi, Marc Peter Deisenroth, K. S. Sesh Kumar:
High-dimensional Bayesian optimization using low-dimensional feature spaces. Mach. Learn. 109(9-10): 1925-1943 (2020) - [j13]Riccardo Moriconi, K. S. Sesh Kumar, Marc Peter Deisenroth:
High-dimensional Bayesian optimization with projections using quantile Gaussian processes. Optim. Lett. 14(1): 51-64 (2020) - [c49]Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth:
Variational Integrator Networks for Physically Structured Embeddings. AISTATS 2020: 3078-3087 - [c48]Samuel Cohen, Rendani Mbuvha, Tshilidzi Marwala, Marc Peter Deisenroth:
Healing Products of Gaussian Process Experts. ICML 2020: 2068-2077 - [c47]Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni:
Stochastic Differential Equations with Variational Wishart Diffusions. ICML 2020: 4974-4983 - [c46]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Efficiently sampling functions from Gaussian process posteriors. ICML 2020: 10292-10302 - [c45]Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Matérn Gaussian Processes on Riemannian Manifolds. NeurIPS 2020 - [c44]Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth:
Probabilistic Active Meta-Learning. NeurIPS 2020 - [i39]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Efficiently sampling functions from Gaussian process posteriors. CoRR abs/2002.09309 (2020) - [i38]Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Matern Gaussian processes on Riemannian manifolds. CoRR abs/2006.10160 (2020) - [i37]Samuel Cohen, Giulia Luise, Alexander Terenin, Brandon Amos, Marc Peter Deisenroth:
Aligning Time Series on Incomparable Spaces. CoRR abs/2006.12648 (2020) - [i36]Martin Jørgensen, Marc Peter Deisenroth, Hugh Salimbeni:
Stochastic Differential Equations with Variational Wishart Diffusions. CoRR abs/2006.14895 (2020) - [i35]Samuel Cohen, Michael Arbel, Marc Peter Deisenroth:
Estimating Barycenters of Measures in High Dimensions. CoRR abs/2007.07105 (2020) - [i34]Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth:
Probabilistic Active Meta-Learning. CoRR abs/2007.08949 (2020) - [i33]Janith C. Petangoda, Nick A. M. Monk, Marc Peter Deisenroth:
A Foliated View of Transfer Learning. CoRR abs/2008.00546 (2020) - [i32]Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth, Nicolas Durrande:
Matern Gaussian Processes on Graphs. CoRR abs/2010.15538 (2020) - [i31]James T. Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, Marc Peter Deisenroth:
Pathwise Conditioning of Gaussian Processes. CoRR abs/2011.04026 (2020) - [i30]Daniel Lengyel, Janith C. Petangoda, Isak Falk, Kate Highnam, Michalis Lazarou, Arinbjörn Kolbeinsson, Marc Peter Deisenroth, Nicholas R. Jennings:
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability. CoRR abs/2011.07407 (2020)
2010 – 2019
- 2019
- [j12]Simon Olofsson, Lukas Hebing, Sebastian Niedenführ, Marc Peter Deisenroth, Ruth Misener:
GPdoemd: A Python package for design of experiments for model discrimination. Comput. Chem. Eng. 125: 54-70 (2019) - [j11]Simon Olofsson, Mohammad Mehrian, Roberto Calandra, Liesbet Geris, Marc Peter Deisenroth, Ruth Misener:
Bayesian Multiobjective Optimisation With Mixed Analytical and Black-Box Functions: Application to Tissue Engineering. IEEE Trans. Biomed. Eng. 66(3): 727-739 (2019) - [c43]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. ICML 2019: 5589-5598 - [i29]Riccardo Moriconi, K. S. Sesh Kumar, Marc Peter Deisenroth:
High-Dimensional Bayesian Optimization with Manifold Gaussian Processes. CoRR abs/1902.10675 (2019) - [i28]K. S. Sesh Kumar, Marc Peter Deisenroth:
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms. CoRR abs/1905.04873 (2019) - [i27]Hugh Salimbeni, Vincent Dutordoir, James Hensman, Marc Peter Deisenroth:
Deep Gaussian Processes with Importance-Weighted Variational Inference. CoRR abs/1905.05435 (2019) - [i26]Steindór Sæmundsson, Alexander Terenin, Katja Hofmann, Marc Peter Deisenroth:
Variational Integrator Networks for Physically Meaningful Embeddings. CoRR abs/1910.09349 (2019) - 2018
- [c42]Sanket Kamthe, Marc Peter Deisenroth:
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. AISTATS 2018: 1701-1710 - [c41]Simon Olofsson, Marc Peter Deisenroth, Ruth Misener:
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches. ICML 2018: 3905-3914 - [c40]Vincent Dutordoir, Hugh Salimbeni, James Hensman, Marc Peter Deisenroth:
Gaussian Process Conditional Density Estimation. NeurIPS 2018: 2391-2401 - [c39]Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth:
Orthogonally Decoupled Variational Gaussian Processes. NeurIPS 2018: 8725-8734 - [c38]James T. Wilson, Frank Hutter, Marc Peter Deisenroth:
Maximizing acquisition functions for Bayesian optimization. NeurIPS 2018: 9906-9917 - [c37]Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth:
Meta Reinforcement Learning with Latent Variable Gaussian Processes. UAI 2018: 642-652 - [i25]Steindór Sæmundsson, Katja Hofmann, Marc Peter Deisenroth:
Meta Reinforcement Learning with Latent Variable Gaussian Processes. CoRR abs/1803.07551 (2018) - [i24]James T. Wilson, Frank Hutter, Marc Peter Deisenroth:
Maximizing acquisition functions for Bayesian optimization. CoRR abs/1805.10196 (2018) - [i23]Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Peter Deisenroth:
Orthogonally Decoupled Variational Gaussian Processes. CoRR abs/1809.08820 (2018) - [i22]Vincent Dutordoir, Hugh Salimbeni, Marc Peter Deisenroth, James Hensman:
Gaussian Process Conditional Density Estimation. CoRR abs/1810.12750 (2018) - 2017
- [j10]Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Ai Poh Loh, Prahlad Vadakkepat, Gerhard Neumann:
Model-based contextual policy search for data-efficient generalization of robot skills. Artif. Intell. 247: 415-439 (2017) - [j9]Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath:
Deep Reinforcement Learning: A Brief Survey. IEEE Signal Process. Mag. 34(6): 26-38 (2017) - [j8]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Gaussian Process Domain Experts for Modeling of Facial Affect. IEEE Trans. Image Process. 26(10): 4697-4711 (2017) - [c36]Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth:
Customer Lifetime Value Prediction Using Embeddings. KDD 2017: 1753-1762 - [c35]Hugh Salimbeni, Marc Peter Deisenroth:
Doubly Stochastic Variational Inference for Deep Gaussian Processes. NIPS 2017: 4588-4599 - [c34]Stefanos Eleftheriadis, Tom Nicholson, Marc Peter Deisenroth, James Hensman:
Identification of Gaussian Process State Space Models. NIPS 2017: 5309-5319 - [c33]Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth:
Probabilistic Inference of Twitter Users' Age Based on What They Follow. ECML/PKDD (3) 2017: 191-203 - [i21]Benjamin Paul Chamberlain, Ângelo Cardoso, C. H. Bryan Liu, Roberto Pagliari, Marc Peter Deisenroth:
Customer Life Time Value Prediction Using Embeddings. CoRR abs/1703.02596 (2017) - [i20]Benjamin Paul Chamberlain, James R. Clough, Marc Peter Deisenroth:
Neural Embeddings of Graphs in Hyperbolic Space. CoRR abs/1705.10359 (2017) - [i19]Sanket Kamthe, Marc Peter Deisenroth:
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control. CoRR abs/1706.06491 (2017) - [i18]Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath:
A Brief Survey of Deep Reinforcement Learning. CoRR abs/1708.05866 (2017) - [i17]James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc Peter Deisenroth:
The reparameterization trick for acquisition functions. CoRR abs/1712.00424 (2017) - 2016
- [j7]Roberto Calandra, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
Bayesian optimization for learning gaits under uncertainty - An experimental comparison on a dynamic bipedal walker. Ann. Math. Artif. Intell. 76(1-2): 5-23 (2016) - [c32]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units. ACCV (2) 2016: 154-170 - [c31]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis. CVPR Workshops 2016: 1469-1477 - [c30]Maciej Kurek, Marc Peter Deisenroth, Wayne Luk, Timothy John Todman:
Knowledge Transfer in Automatic Optimisation of Reconfigurable Designs. FCCM 2016: 84-87 - [c29]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for regression. IJCNN 2016: 3338-3345 - [c28]Maja Pantic, Vanessa Evers, Marc Peter Deisenroth, Luis Merino, Björn W. Schuller:
Social and Affective Robotics Tutorial. ACM Multimedia 2016: 1477-1478 - [i16]Benjamin Paul Chamberlain, Josh Levy-Kramer, Clive Humby, Marc Peter Deisenroth:
Real-Time Association Mining in Large Social Networks. CoRR abs/1601.03958 (2016) - [i15]Benjamin Paul Chamberlain, Clive Humby, Marc Peter Deisenroth:
Detecting the Age of Twitter Users. CoRR abs/1601.04621 (2016) - [i14]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis. CoRR abs/1604.02917 (2016) - [i13]Stefanos Eleftheriadis, Ognjen Rudovic, Marc Peter Deisenroth, Maja Pantic:
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units. CoRR abs/1608.04664 (2016) - 2015
- [j6]Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen:
Gaussian Processes for Data-Efficient Learning in Robotics and Control. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 408-423 (2015) - [c27]Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Jan Peters:
Learning torque control in presence of contacts using tactile sensing from robot skin. Humanoids 2015: 690-695 - [c26]Marc Peter Deisenroth, Jun Wei Ng:
Distributed Gaussian Processes. ICML 2015: 1481-1490 - [c25]Roberto Calandra, Serena Ivaldi, Marc Peter Deisenroth, Elmar Rueckert, Jan Peters:
Learning inverse dynamics models with contacts. ICRA 2015: 3186-3191 - [i12]Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
From Pixels to Torques: Policy Learning with Deep Dynamical Models. CoRR abs/1502.02251 (2015) - [i11]Marc Peter Deisenroth, Dieter Fox, Carl Edward Rasmussen:
Gaussian Processes for Data-Efficient Learning in Robotics and Control. CoRR abs/1502.02860 (2015) - [i10]John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. CoRR abs/1510.02173 (2015) - [i9]Doniyor Ulmasov, Caroline Baroukh, Benoît Chachuat, Marc Peter Deisenroth, Ruth Misener:
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems. CoRR abs/1511.05385 (2015) - 2014
- [c24]Nooshin HajiGhassemi, Marc Peter Deisenroth:
Analytic Long-Term Forecasting with Periodic Gaussian Processes. AISTATS 2014: 303-311 - [c23]Sanket Kamthe, Jan Peters, Marc Peter Deisenroth:
Multi-modal filtering for non-linear estimation. ICASSP 2014: 7979-7983 - [c22]Roberto Calandra, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
An experimental comparison of Bayesian optimization for bipedal locomotion. ICRA 2014: 1951-1958 - [c21]Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox:
Multi-task policy search for robotics. ICRA 2014: 3876-3881 - [c20]Bastian Bischoff, Duy Nguyen-Tuong, Herke van Hoof, Andrew McHutchon, Carl E. Rasmussen, Alois C. Knoll, Jan Peters, Marc Peter Deisenroth:
Policy search for learning robot control using sparse data. ICRA 2014: 3882-3887 - [c19]Roberto Calandra, Nakul Gopalan, André Seyfarth, Jan Peters, Marc Peter Deisenroth:
Bayesian Gait Optimization for Bipedal Locomotion. LION 2014: 274-290 - [i8]Sanket Kamthe, Jan Peters, Marc Peter Deisenroth:
Multi-modal filtering for non-linear estimation. CoRR abs/1401.0077 (2014) - [i7]Roberto Calandra, Jan Peters, Carl Edward Rasmussen, Marc Peter Deisenroth:
Manifold Gaussian Processes for Regression. CoRR abs/1402.5876 (2014) - [i6]Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth:
Learning deep dynamical models from image pixels. CoRR abs/1410.7550 (2014) - [i5]Jun Wei Ng, Marc Peter Deisenroth:
Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression. CoRR abs/1412.3078 (2014) - 2013
- [j5]Peter Englert, Alexandros Paraschos, Marc Peter Deisenroth, Jan Peters:
Probabilistic model-based imitation learning. Adapt. Behav. 21(5): 388-403 (2013) - [j4]Marc Peter Deisenroth, Gerhard Neumann, Jan Peters:
A Survey on Policy Search for Robotics. Found. Trends Robotics 2(1-2): 1-142 (2013) - [j3]Zhikun Wang, Katharina Mülling, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic movement modeling for intention inference in human-robot interaction. Int. J. Robotics Res. 32(7): 841-858 (2013) - [c18]Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Gerhard Neumann:
Data-Efficient Generalization of Robot Skills with Contextual Policy Search. AAAI 2013: 1401-1407 - [c17]Nakul Gopalan, Marc Peter Deisenroth, Jan Peters:
Feedback error learning for rhythmic motor primitives. ICRA 2013: 1317-1322 - [c16]Peter Englert, Alexandros Paraschos, Jan Peters, Marc Peter Deisenroth:
Model-based imitation learning by probabilistic trajectory matching. ICRA 2013: 1922-1927 - [i4]Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox:
Multi-Task Policy Search. CoRR abs/1307.0813 (2013) - 2012
- [j2]Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen:
Robust Filtering and Smoothing with Gaussian Processes. IEEE Trans. Autom. Control. 57(7): 1865-1871 (2012) - [c15]Marc Peter Deisenroth, Csaba Szepesvári, Jan Peters:
Preface. EWRL 2012 - [c14]Roberto Calandra, Tapani Raiko, Marc Peter Deisenroth, Federico Montesino-Pouzols:
Learning Deep Belief Networks from Non-stationary Streams. ICANN (2) 2012: 379-386 - [c13]Marc Peter Deisenroth, Roberto Calandra, André Seyfarth, Jan Peters:
Toward fast policy search for learning legged locomotion. IROS 2012: 1787-1792 - [c12]Marc Peter Deisenroth, Shakir Mohamed:
Expectation Propagation in Gaussian Process Dynamical Systems. NIPS 2012: 2618-2626 - [c11]Zhikun Wang, Marc Peter Deisenroth, Heni Ben Amor, David Vogt, Bernhard Schölkopf, Jan Peters:
Probabilistic Modeling of Human Movements for Intention Inference. Robotics: Science and Systems 2012 - [e1]Marc Peter Deisenroth, Csaba Szepesvári, Jan Peters:
Proceedings of the Tenth European Workshop on Reinforcement Learning, EWRL 2012, Edinburgh, Scotland, UK, June, 2012. JMLR Proceedings 24, JMLR.org 2012 [contents] - [i3]Marc Peter Deisenroth, Ryan D. Turner, Marco F. Huber, Uwe D. Hanebeck, Carl Edward Rasmussen:
Robust Filtering and Smoothing with Gaussian Processes. CoRR abs/1203.4345 (2012) - [i2]Marc Peter Deisenroth, Shakir Mohamed:
Expectation Propagation in Gaussian Process Dynamical Systems. CoRR abs/1207.2940 (2012) - 2011
- [c10]Marc Peter Deisenroth, Henrik Ohlsson:
A general perspective on Gaussian filtering and smoothing: Explaining current and deriving new algorithms. ACC 2011: 1807-1812 - [c9]Marc Peter Deisenroth, Carl Edward Rasmussen:
PILCO: A Model-Based and Data-Efficient Approach to Policy Search. ICML 2011: 465-472 - [c8]Cynthia Matuszek, Brian Mayton, Roberto Aimi, Marc Peter Deisenroth, Liefeng Bo, Robert Chu, Mike Kung, Louis LeGrand, Joshua R. Smith, Dieter Fox:
Gambit: An autonomous chess-playing robotic system. ICRA 2011: 4291-4297 - [c7]Marc Peter Deisenroth, Carl Edward Rasmussen, Dieter Fox:
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning. Robotics: Science and Systems 2011 - 2010
- [b1]Marc Peter Deisenroth:
Efficient reinforcement learning using Gaussian processes. Karlsruhe Institute of Technology, 2010, ISBN 978-3-86644-569-7, pp. 1-205 - [c6]Ryan D. Turner, Marc Peter Deisenroth, Carl Edward Rasmussen:
State-Space Inference and Learning with Gaussian Processes. AISTATS 2010: 868-875 - [i1]Marc Peter Deisenroth, Henrik Ohlsson:
A Probabilistic Perspective on Gaussian Filtering and Smoothing. CoRR abs/1006.2165 (2010)
2000 – 2009
- 2009
- [j1]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Gaussian process dynamic programming. Neurocomputing 72(7-9): 1508-1524 (2009) - [c5]Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hanebeck:
Analytic moment-based Gaussian process filtering. ICML 2009: 225-232 - 2008
- [c4]Marc Peter Deisenroth, Jan Peters, Carl E. Rasmussen:
Approximate dynamic programming with Gaussian processes. ACC 2008: 4480-4485 - [c3]Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters:
Model-Based Reinforcement Learning with Continuous States and Actions. ESANN 2008: 19-24 - [c2]Carl Edward Rasmussen, Marc Peter Deisenroth:
Probabilistic Inference for Fast Learning in Control. EWRL 2008: 229-242 - 2006
- [c1]Marc Peter Deisenroth, Toshiyuki Ohtsuka, Florian Weissel, Dietrich Brunn, Uwe D. Hanebeck:
Finite-Horizon Optimal State-Feedback Control of Nonlinear Stochastic Systems Based on a Minimum Principle. MFI 2006: 371-376
Coauthor Index
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