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ORCIDCarl Henrik Ek
Person information
- affiliation: University of Bristol, Department of Computer Science, UK
Other persons with a similar name
- Henrik Ekeus 0001 (aka: Henrik Carl-Olof Julian Oldfeldt Ekéus) — Queen Mary University of London, UK
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2020 – today
- 2024
[j11]Sonja Schlenz
, Simon Mößner, Carl Henrik Ek, Fabian Duddeck:
Representing engineering design changes in finite element models using directed point cloud autoencoders. Adv. Eng. Informatics 59: 102259 (2024)- [i35]Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg:
Reparameterization invariance in approximate Bayesian inference. CoRR abs/2406.03334 (2024) - [i34]Erik Bodin, Henry Moss, Carl Henrik Ek:
Linear combinations of latents in diffusion models: interpolation and beyond. CoRR abs/2408.08558 (2024) - [i33]Paul Jeha, Will Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen:
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate. CoRR abs/2408.12270 (2024) - [i32]Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok:
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling. CoRR abs/2410.04988 (2024) - [i31]Carl Henrik Ek, Oisin Kim, Challenger Mishra:
Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithm. CoRR abs/2410.11284 (2024) - 2023
- [j10]Haoting Zhang, Carl Henrik Ek, Magnus Rattray, Marta Milo:
SynBa: improved estimation of drug combination synergies with uncertainty quantification. Bioinform. 39(Supplement-1): 121-130 (2023) - [j9]Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg:
Identifying latent distances with Finslerian geometry. Trans. Mach. Learn. Res. 2023 (2023) - [c57]Aidan Scannell, Carl Henrik Ek, Arthur Richards:
Mode-constrained Model-based Reinforcement Learning via Gaussian Processes. AISTATS 2023: 3299-3314 - [c56]Sonja Schlenz, Simon Mößner, Carl Henrik Ek, Fabian Duddeck:
A Flexible Approach for Retrieving Geometrically Similar Finite Element Models Using Point Cloud Autoencoders. KDIR 2023: 188-195 - 2022
- [c55]Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell:
Aligned Multi-Task Gaussian Process. AISTATS 2022: 2970-2988 - [i30]Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite:
Optimisation of a global climate model ensemble for prediction of extreme heat days. CoRR abs/2211.16367 (2022) - [i29]Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg:
Identifying latent distances with Finslerian geometry. CoRR abs/2212.10010 (2022) - 2021
- [j8]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. J. Mach. Learn. Res. 22: 86:1-86:51 (2021) - [c54]Erik Bodin, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek:
Black-box density function estimation using recursive partitioning. ICML 2021: 1015-1025 - [c53]Aidan Scannell, Carl Henrik Ek, Arthur Richards:
Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation. ICRA 2021: 12745-12751 - [c52]Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande:
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. NeurIPS 2021: 9443-9455 - [i28]Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande:
Deep Neural Networks as Point Estimates for Deep Gaussian Processes. CoRR abs/2105.04504 (2021) - [i27]Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell:
Aligned Multi-Task Gaussian Process. CoRR abs/2110.15761 (2021) - 2020
- [j7]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian decomposition of multi-modal dynamical systems for reinforcement learning. Neurocomputing 416: 352-359 (2020) - [j6]Gabriela Zarzar Gandler, Carl Henrik Ek, Mårten Björkman, Rustam Stolkin, Yasemin Bekiroglu:
Object shape estimation and modeling, based on sparse Gaussian process implicit surfaces, combining visual data and tactile exploration. Robotics Auton. Syst. 126: 103433 (2020) - [c51]Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell:
Monotonic Gaussian Process Flows. AISTATS 2020: 3057-3067 - [c50]Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek:
Modulating Surrogates for Bayesian Optimization. ICML 2020: 970-979 - [c49]Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek:
Compositional uncertainty in deep Gaussian processes. UAI 2020: 480-489 - [i26]Olga Mikheeva, Ieva Kazlauskaite, Hedvig Kjellström, Carl Henrik Ek:
Bayesian nonparametric shared multi-sequence time series segmentation. CoRR abs/2001.09886 (2020) - [i25]Erik Bodin, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek:
Black-box density function estimation using recursive partitioning. CoRR abs/2010.13632 (2020)
2010 – 2019
- 2019
- [c48]Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell:
Gaussian Process Latent Variable Alignment Learning. AISTATS 2019: 748-757 - [c47]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
interpretable dynamics models for data-efficient reinforcement learning. ESANN 2019 - [c46]Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell:
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. ICML 2019: 3682-3691 - [c45]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Data Association with Gaussian Processes. ECML/PKDD (2) 2019: 548-564 - [i24]Martin Hjelm, Carl Henrik Ek, Renaud Detry, Danica Kragic:
Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning. CoRR abs/1901.10673 (2019) - [i23]Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell:
Monotonic Gaussian Process Flow. CoRR abs/1905.12930 (2019) - [i22]Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Neill D. F. Campbell, Carl Henrik Ek:
Modulated Bayesian Optimization using Latent Gaussian Process Models. CoRR abs/1906.11152 (2019) - [i21]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning. CoRR abs/1907.04902 (2019) - [i20]Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek:
Compositional uncertainty in deep Gaussian processes. CoRR abs/1909.07698 (2019) - 2018
- [c44]Alessandro Di Martino, Erik Bodin, Carl Henrik Ek, Neill D. F. Campbell:
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. ACCV (4) 2018: 3-20 - [c43]Olga Mikheeva, Carl Henrik Ek, Hedvig Kjellström:
Perceptual Facial Expression Representation. FG 2018: 179-186 - [c42]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian Alignments of Warped Multi-Output Gaussian Processes. NeurIPS 2018: 6995-7004 - [i19]Sergio Caccamo, Yasemin Bekiroglu, Carl Henrik Ek, Danica Kragic:
Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces. CoRR abs/1802.04642 (2018) - [i18]Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell:
Gaussian Process Latent Variable Alignment Learning. CoRR abs/1803.02603 (2018) - [i17]Song Liu, Wittawat Jitkrittum, Carl Henrik Ek:
Model Inference with Stein Density Ratio Estimation. CoRR abs/1805.07454 (2018) - [i16]Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell:
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. CoRR abs/1807.04833 (2018) - [i15]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Multimodal Deep Gaussian Processes. CoRR abs/1810.07158 (2018) - [i14]Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek, Neill D. F. Campbell:
Sequence Alignment with Dirichlet Process Mixtures. CoRR abs/1811.10689 (2018) - [i13]Alessandro Di Martino, Erik Bodin, Carl Henrik Ek, Neill D. F. Campbell:
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. CoRR abs/1812.05477 (2018) - 2017
- [i12]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Manifold Alignment Determination: finding correspondences across different data views. CoRR abs/1701.03449 (2017) - [i11]Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek:
Latent Gaussian Process Regression. CoRR abs/1707.05534 (2017) - [i10]Iman Malik, Carl Henrik Ek:
Neural Translation of Musical Style. CoRR abs/1708.03535 (2017) - [i9]Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek:
Bayesian Alignments of Warped Multi-Output Gaussian Processes. CoRR abs/1710.02766 (2017) - [i8]Erik Bodin, Iman Malik, Carl Henrik Ek, Neill D. F. Campbell:
Nonparametric Inference for Auto-Encoding Variational Bayes. CoRR abs/1712.06536 (2017) - 2016
- [c41]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek:
Inter-battery Topic Representation Learning. ECCV (8) 2016: 210-226 - [c40]Yasemin Bekiroglu, Andreas C. Damianou, Renaud Detry, Johannes A. Stork, Danica Kragic, Carl Henrik Ek:
Probabilistic consolidation of grasp experience. ICRA 2016: 193-200 - [c39]Sergio Caccamo, Yasemin Bekiroglu, Carl Henrik Ek, Danica Kragic:
Active exploration using Gaussian Random Fields and Gaussian Process Implicit Surfaces. IROS 2016: 582-589 - [c38]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson:
Diagnostic Prediction Using Discomfort Drawings with IBTM. MLHC 2016: 226-238 - [i7]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. CoRR abs/1604.04939 (2016) - [i6]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek:
Inter-Battery Topic Representation Learning. CoRR abs/1605.06155 (2016) - [i5]Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil D. Lawrence:
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model. CoRR abs/1607.00067 (2016) - [i4]Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo C. Bertilson:
Diagnostic Prediction Using Discomfort Drawings with IBTM. CoRR abs/1607.08206 (2016) - 2015
- [j5]Dan Song, Carl Henrik Ek, Kai Huebner, Danica Kragic:
Task-Based Robot Grasp Planning Using Probabilistic Inference. IEEE Trans. Robotics 31(3): 546-561 (2015) - [c37]Johannes A. Stork, Carl Henrik Ek, Yasemin Bekiroglu, Danica Kragic:
Learning Predictive State Representation for in-hand manipulation. ICRA 2015: 3207-3214 - [c36]Martin Hjelm, Carl Henrik Ek, Renaud Detry, Danica Kragic:
Learning Human Priors for Task-Constrained Grasping. ICVS 2015: 207-217 - [c35]Johannes A. Stork, Carl Henrik Ek, Danica Kragic:
Learning Predictive State Representations for planning. IROS 2015: 3427-3434 - [c34]Andreas C. Damianou, Carl Henrik Ek, Luke Boorman, Neil D. Lawrence, Tony J. Prescott:
A Top-Down Approach for a Synthetic Autobiographical Memory System. Living Machines 2015: 280-292 - [c33]Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, Stefan Carlsson:
Persistent Evidence of Local Image Properties in Generic ConvNets. SCIA 2015: 249-262 - [i3]Andrea Baisero, Florian T. Pokorny, Carl Henrik Ek:
On a Family of Decomposable Kernels on Sequences. CoRR abs/1501.06284 (2015) - 2014
- [j4]Mitesh Patel, Jaime Valls Miró, Danica Kragic, Carl Henrik Ek, Gamini Dissanayake:
Learning object, grasping and manipulation activities using hierarchical HMMs. Auton. Robots 37(3): 317-331 (2014) - [c32]Alessandro Pieropan, Carl Henrik Ek, Hedvig Kjellström:
Recognizing object affordances in terms of spatio-temporal object-object relationships. Humanoids 2014: 52-58 - [c31]Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson:
A Topological Framework for Training Latent Variable Models. ICPR 2014: 2471-2476 - [c30]Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson:
Initialization Framework for Latent Variable Models. ICPRAM 2014: 227-232 - [c29]Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson:
Gradual Improvement of Image Descriptor Quality. ICPRAM 2014: 233-238 - [c28]Martin Hjelm, Renaud Detry, Carl Henrik Ek, Danica Kragic:
Representations for cross-task, cross-object grasp transfer. ICRA 2014: 5699-5704 - [i2]Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, Stefan Carlsson:
Persistent Evidence of Local Image Properties in Generic ConvNets. CoRR abs/1411.6509 (2014) - 2013
- [j3]Javier Romero, Hedvig Kjellström, Carl Henrik Ek, Danica Kragic:
Non-parametric hand pose estimation with object context. Image Vis. Comput. 31(8): 555-564 (2013) - [j2]Thomas Feix, Javier Romero, Carl Henrik Ek, Heinz-Bodo Schmiedmayer, Danica Kragic:
A Metric for Comparing the Anthropomorphic Motion Capability of Artificial Hands. IEEE Trans. Robotics 29(1): 82-93 (2013) - [j1]Javier Romero, Thomas Feix, Carl Henrik Ek, Hedvig Kjellström, Danica Kragic:
Extracting Postural Synergies for Robotic Grasping. IEEE Trans. Robotics 29(6): 1342-1352 (2013) - [c27]Akshaya Thippur, Carl Henrik Ek, Hedvig Kjellström:
Inferring hand pose: A comparative study of visual shape features. FG 2013: 1-8 - [c26]Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian T. Pokorny, Hedvig Kjellström:
Supervised Hierarchical Dirichlet Processes with Variational Inference. ICCV Workshops 2013: 254-261 - [c25]Andrea Baisero, Florian T. Pokorny, Danica Kragic, Carl Henrik Ek:
The Path Kernel. ICPRAM 2013: 50-57 - [c24]Andrea Baisero, Florian T. Pokorny, Danica Kragic, Carl Henrik Ek:
The Path Kernel: A Novel Kernel for Sequential Data. ICPRAM (Selected Papers) 2013: 71-84 - [c23]Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson:
Qualitative Vocabulary based Descriptor. ICPRAM 2013: 188-193 - [c22]Renaud Detry, Carl Henrik Ek, Marianna Madry, Danica Kragic:
Learning a dictionary of prototypical grasp-predicting parts from grasping experience. ICRA 2013: 601-608 - [c21]Martin Hjelm, Carl Henrik Ek, Renaud Detry, Hedvig Kjellström, Danica Kragic:
Sparse summarization of robotic grasping data. ICRA 2013: 1082-1087 - [c20]Alessandro Pieropan, Carl Henrik Ek, Hedvig Kjellström:
Functional object descriptors for human activity modeling. ICRA 2013: 1282-1289 - [c19]Mitesh Patel, Carl Henrik Ek, Nikolaos Kyriazis, Antonis A. Argyros, Jaime Valls Miró, Danica Kragic:
Language for learning complex human-object interactions. ICRA 2013: 4997-5002 - [c18]Marianna Madry, Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson, Danica Kragic:
Extracting essential local object characteristics for 3D object categorization. IROS 2013: 2240-2247 - [c17]Cheng Zhang, Carl Henrik Ek, Hedvig Kjellström:
Factorized Topic Models. ICLR (Workshop Poster) 2013 - 2012
- [c16]Alexander Davies, Carl Henrik Ek, Colin J. Dalton, Neill W. Campbell:
Generating 3D Morphable Model Parameters for Facial Tracking - Factorising Identity and Expression. GRAPP/IVAPP 2012: 309-318 - [c15]Niklas Bergström, Carl Henrik Ek, Danica Kragic, Yuji Yamakawa, Taku Senoo, Masatoshi Ishikawa:
On-line learning of temporal state models for flexible objects. Humanoids 2012: 712-718 - [c14]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. ICML 2012 - [c13]Renaud Detry, Carl Henrik Ek, Marianna Madry, Justus H. Piater, Danica Kragic:
Generalizing grasps across partly similar objects. ICRA 2012: 3791-3797 - [c12]Marianna Madry, Carl Henrik Ek, Renaud Detry, Kaiyu Hang, Danica Kragic:
Improving generalization for 3D object categorization with Global Structure Histograms. IROS 2012: 1379-1386 - [c11]Florian T. Pokorny, Carl Henrik Ek, Hedvig Kjellström, Danica Kragic:
Persistent Homology for Learning Densities with Bounded Support. NIPS 2012: 1826-1834 - [i1]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. CoRR abs/1206.4610 (2012) - 2011
- [c10]Dan Song, Carl Henrik Ek, Kai Huebner, Danica Kragic:
Multivariate discretization for Bayesian Network structure learning in robot grasping. ICRA 2011: 1944-1950 - [c9]Niklas Bergström, Carl Henrik Ek, Mårten Björkman, Danica Kragic:
Scene Understanding through Autonomous Interactive Perception. ICVS 2011: 153-162 - [c8]Dan Song, Carl Henrik Ek, Kai Huebner, Danica Kragic:
Embodiment-specific representation of robot grasping using graphical models and latent-space discretization. IROS 2011: 980-986 - [c7]Guoliang Luo, Niklas Bergström, Carl Henrik Ek, Danica Kragic:
Representing actions with Kernels. IROS 2011: 2028-2035 - [c6]Carl Henrik Ek, Danica Kragic:
The Importance of Structure. ISRR 2011: 111-127 - [c5]Alexander Davies, Carl Henrik Ek, Colin J. Dalton, Neill W. Campbell:
Facial Movement Based Recognition. MIRAGE 2011: 51-62 - 2010
- [c4]Carl Henrik Ek, Dan Song, Kai Huebner, Danica Kragic:
Task modeling in imitation learning using latent variable models. Humanoids 2010: 548-553 - [c3]Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell:
Factorized Orthogonal Latent Spaces. AISTATS 2010: 701-708
2000 – 2009
- 2008
- [c2]Carl Henrik Ek, Jonathan Rihan, Philip H. S. Torr, Grégory Rogez, Neil D. Lawrence:
Ambiguity Modeling in Latent Spaces. MLMI 2008: 62-73 - 2007
- [c1]Carl Henrik Ek, Philip H. S. Torr, Neil D. Lawrence:
Gaussian Process Latent Variable Models for Human Pose Estimation. MLMI 2007: 132-143
Coauthor Index
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