Ek, Carl Henrik

24 publications

ICLR 2025 Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling Jasmine Bayrooti, Carl Henrik Ek, Amanda Prorok

ICLR 2025 Linear Combinations of Latents in Generative Models: Subspaces and Beyond Erik Bodin, Alexandru I. Stere, Dragos D Margineantu, Carl Henrik Ek, Henry Moss

NeurIPS 2025 No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes Jasmine Bayrooti, Sattar Vakili, Amanda Prorok, Carl Henrik Ek

NeurIPS 2025 VIKING: Deep Variational Inference with Stochastic Projections Samuel G. Fadel, Hrittik Roy, Nicholas Krämer, Yevgen Zainchkovskyy, Stas Syrota, Alejandro Valverde Mahou, Carl Henrik Ek, Søren Hauberg

NeurIPS 2024 Reparameterization Invariance in Approximate Bayesian Inference Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg

ICMLW 2024 Variance Reduction of Diffusion Model's Gradients with Taylor Approximation-Based Control Variate Paul Jeha, Will Sussman Grathwohl, Michael Riis Andersen, Carl Henrik Ek, Jes Frellsen

TMLR 2023 Identifying Latent Distances with Finslerian Geometry Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg

AISTATS 2023 Mode-Constrained Model-Based Reinforcement Learning via Gaussian Processes Aidan Scannell, Carl Henrik Ek, Arthur Richards

NeurIPSW 2022 Identifying Latent Distances with Finslerian Geometry Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg

ICML 2021 Black-Box Density Function Estimation Using Recursive Partitioning Erik Bodin, Zhenwen Dai, Neill Campbell, Carl Henrik Ek

NeurIPS 2021 Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande

JMLR 2021 Multi-View Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek

ICML 2020 Modulating Surrogates for Bayesian Optimization Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill Campbell, Carl Henrik Ek

AISTATS 2020 Monotonic Gaussian Process Flows Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell

ICML 2019 DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures Andrew Lawrence, Carl Henrik Ek, Neill Campbell

ECML-PKDD 2019 Data Association with Gaussian Processes Markus Kaiser, Clemens Otte, Thomas A. Runkler, Carl Henrik Ek

AISTATS 2019 Gaussian Process Latent Variable Alignment Learning Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell

NeurIPS 2018 Bayesian Alignments of Warped Multi-Output Gaussian Processes Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek

MLHC 2016 Diagnostic Prediction Using Discomfort Drawings with IBTM Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek, Bo Bertilson

ECCV 2016 Inter-Battery Topic Representation Learning Cheng Zhang, Hedvig Kjellström, Carl Henrik Ek

ICLR 2013 Factorized Topic Models Cheng Zhang, Carl Henrik Ek, Hedvig Kjellström

ICCVW 2013 Supervised Hierarchical Dirichlet Processes with Variational Inference Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian T. Pokorny, Hedvig Kjellström

ICML 2012 Manifold Relevance Determination Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence

AISTATS 2010 Factorized Orthogonal Latent Spaces Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell