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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
- 2024
[c65]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- [c64]Mathieu Alain, So Takao, Brooks Paige, Marc Peter Deisenroth:
Gaussian Processes on Cellular Complexes. ICML 2024 - [i77]Rafael Anderka, Marc Peter Deisenroth, So Takao:
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems. CoRR abs/2402.17036 (2024) - [i76]Oscar Key, So Takao, Daniel Giles, Marc Peter Deisenroth:
Scalable Data Assimilation with Message Passing. CoRR abs/2404.12968 (2024) - [i75]Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten:
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. CoRR abs/2406.04759 (2024) - [i74]Mirgahney Mohamed, Harry Jake Cunningham, Marc Peter Deisenroth, Lourdes Agapito:
RecMoDiffuse: Recurrent Flow Diffusion for Human Motion Generation. CoRR abs/2406.07169 (2024) - [i73]Vignesh Gopakumar, Joel Oskarrson, Ander Gray, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt Kusner, Marc Peter Deisenroth:
Valid Error Bars for Neural Weather Models using Conformal Prediction. CoRR abs/2406.14483 (2024) - [i72]Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang, Marc Peter Deisenroth:
Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling. CoRR abs/2408.09453 (2024) - [i71]Vignesh Gopakumar, Ander Gray, Joel Oskarsson, Lorenzo Zanisi, Stanislas Pamela, Daniel Giles, Matt Kusner, Marc Peter Deisenroth:
Uncertainty Quantification of Pre-Trained and Fine-Tuned Surrogate Models using Conformal Prediction. CoRR abs/2408.09881 (2024) - [i70]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) - 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 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]