Carl Henrik Ek

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KTH Royal Institute of Technology ∙ University of Bristol

Senior Lecturer in Computer Science at the University of Bristol, UK and a Docent in Machine Learning at the Royal Institute of Technology, Sweden.

Black-box density function estimation using recursive partitioning

We present a novel approach to Bayesian inference and general Bayesian c...
10/26/2020 ∙ by Erik Bodin, et al. ∙ 0 ∙ share

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Bayesian nonparametric shared multi-sequence time series segmentation

In this paper, we introduce a method for segmenting time series data usi...
01/27/2020 ∙ by Olga Mikheeva, et al. ∙ 3 ∙ share

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Compositional uncertainty in deep Gaussian processes

Gaussian processes (GPs) are nonparametric priors over functions, and fi...
09/17/2019 ∙ by Ivan Ustyuzhaninov, et al. ∙ 13 ∙ share

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Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

In this paper, we present a Bayesian view on model-based reinforcement l...
07/10/2019 ∙ by Markus Kaiser, et al. ∙ 5 ∙ share

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Modulated Bayesian Optimization using Latent Gaussian Process Models

We present an approach to Bayesian Optimization that allows for robust s...
06/26/2019 ∙ by Erik Bodin, et al. ∙ 2 ∙ share

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Monotonic Gaussian Process Flow

We propose a new framework of imposing monotonicity constraints in a Bay...
05/30/2019 ∙ by Ivan Ustyuzhaninov, et al. ∙ 0 ∙ share

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Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning

This paper presents an approach for learning invariant features for obje...
01/30/2019 ∙ by Martin Hjelm, et al. ∙ 0 ∙ share

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Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

The shape of an object is an important characteristic for many vision pr...
12/13/2018 ∙ by Alessandro Di Martino, et al. ∙ 14 ∙ share

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Sequence Alignment with Dirichlet Process Mixtures

We present a probabilistic model for unsupervised alignment of high-dime...
11/26/2018 ∙ by Ieva Kazlauskaite, et al. ∙ 0 ∙ share

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Multimodal Deep Gaussian Processes

We propose a novel Bayesian approach to modelling multimodal data genera...
10/16/2018 ∙ by Markus Kaiser, et al. ∙ 0 ∙ share

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DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

We present a non-parametric Bayesian latent variable model capable of le...
07/12/2018 ∙ by Andrew R. Lawrence, et al. ∙ 8 ∙ share

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Model Inference with Stein Density Ratio Estimation

The Kullback-Leilber divergence from model to data is a classic goodness...
05/18/2018 ∙ by Song Liu, et al. ∙ 0 ∙ share

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Gaussian Process Latent Variable Alignment Learning

We present a model that can automatically learn alignments between high-...
03/07/2018 ∙ by Ieva Kazlauskaite, et al. ∙ 0 ∙ share

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Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces

In this work we study the problem of exploring surfaces and building com...
02/13/2018 ∙ by Sergio Caccamo, et al. ∙ 0 ∙ share

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Nonparametric Inference for Auto-Encoding Variational Bayes

We would like to learn latent representations that are low-dimensional a...
12/18/2017 ∙ by Erik Bodin, et al. ∙ 0 ∙ share

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Bayesian Alignments of Warped Multi-Output Gaussian Processes

We present a Bayesian extension to convolution processes which defines a...
10/08/2017 ∙ by Markus Kaiser, et al. ∙ 0 ∙ share

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Neural Translation of Musical Style

Music is an expressive form of communication often used to convey emotio...
08/11/2017 ∙ by Iman Malik, et al. ∙ 0 ∙ share

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Latent Gaussian Process Regression

We introduce Latent Gaussian Process Regression which is a latent variab...
07/18/2017 ∙ by Erik Bodin, et al. ∙ 0 ∙ share

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Manifold Alignment Determination: finding correspondences across different data views

We present Manifold Alignment Determination (MAD), an algorithm for lear...
01/12/2017 ∙ by Andreas Damianou, et al. ∙ 0 ∙ share

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Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Unsupervised learning on imbalanced data is challenging because, when gi...
06/30/2016 ∙ by Fariba Yousefi, et al. ∙ 0 ∙ share

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Inter-Battery Topic Representation Learning

In this paper, we present the Inter-Battery Topic Model (IBTM). Our appr...
05/19/2016 ∙ by Cheng Zhang, et al. ∙ 0 ∙ share

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Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis

Factor analysis aims to determine latent factors, or traits, which summa...
04/17/2016 ∙ by Andreas Damianou, et al. ∙ 0 ∙ share

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Persistent Evidence of Local Image Properties in Generic ConvNets

Supervised training of a convolutional network for object classification...
11/24/2014 ∙ by Ali Sharif Razavian, et al. ∙ 0 ∙ share

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Factorized Topic Models

In this paper we present a modification to a latent topic model, which m...
01/15/2013 ∙ by Cheng Zhang, et al. ∙ 0 ∙ share

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Carl Henrik Ek

Featured Co-authors

Cheng Zhang

Danica Kragic

Neil D. Lawrence

Hedvig Kjellström

Wittawat Jitkrittum

Song Liu

Andreas Damianou

Zhenwen Dai

Neill D. F. Campbell

Neil Lawrence

Hossein Azizpour

Black-box density function estimation using recursive partitioning

Bayesian nonparametric shared multi-sequence time series segmentation

Compositional uncertainty in deep Gaussian processes

Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

Modulated Bayesian Optimization using Latent Gaussian Process Models

Monotonic Gaussian Process Flow

Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning

Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation

Sequence Alignment with Dirichlet Process Mixtures

Multimodal Deep Gaussian Processes

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

Model Inference with Stein Density Ratio Estimation

Gaussian Process Latent Variable Alignment Learning

Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces

Nonparametric Inference for Auto-Encoding Variational Bayes

Bayesian Alignments of Warped Multi-Output Gaussian Processes

Neural Translation of Musical Style

Latent Gaussian Process Regression

Manifold Alignment Determination: finding correspondences across different data views

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Inter-Battery Topic Representation Learning

Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis

Persistent Evidence of Local Image Properties in Generic ConvNets

Factorized Topic Models