
Blackbox density function estimation using recursive partitioning
We present a novel approach to Bayesian inference and general Bayesian c...
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Bayesian nonparametric shared multisequence time series segmentation
In this paper, we introduce a method for segmenting time series data usi...
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Compositional uncertainty in deep Gaussian processes
Gaussian processes (GPs) are nonparametric priors over functions, and fi...
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Interpretable Dynamics Models for DataEfficient Reinforcement Learning
In this paper, we present a Bayesian view on modelbased reinforcement l...
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Modulated Bayesian Optimization using Latent Gaussian Process Models
We present an approach to Bayesian Optimization that allows for robust s...
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Monotonic Gaussian Process Flow
We propose a new framework of imposing monotonicity constraints in a Bay...
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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...
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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...
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Sequence Alignment with Dirichlet Process Mixtures
We present a probabilistic model for unsupervised alignment of highdime...
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Multimodal Deep Gaussian Processes
We propose a novel Bayesian approach to modelling multimodal data genera...
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DPGPLVM: A Bayesian NonParametric Model for Learning Multivariate Dependency Structures
We present a nonparametric Bayesian latent variable model capable of le...
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Model Inference with Stein Density Ratio Estimation
The KullbackLeilber divergence from model to data is a classic goodness...
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Gaussian Process Latent Variable Alignment Learning
We present a model that can automatically learn alignments between high...
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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...
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Nonparametric Inference for AutoEncoding Variational Bayes
We would like to learn latent representations that are lowdimensional a...
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Bayesian Alignments of Warped MultiOutput Gaussian Processes
We present a Bayesian extension to convolution processes which defines a...
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Neural Translation of Musical Style
Music is an expressive form of communication often used to convey emotio...
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Latent Gaussian Process Regression
We introduce Latent Gaussian Process Regression which is a latent variab...
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Manifold Alignment Determination: finding correspondences across different data views
We present Manifold Alignment Determination (MAD), an algorithm for lear...
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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...
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InterBattery Topic Representation Learning
In this paper, we present the InterBattery Topic Model (IBTM). Our appr...
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Multiview Learning as a Nonparametric Nonlinear InterBattery Factor Analysis
Factor analysis aims to determine latent factors, or traits, which summa...
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Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification...
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Factorized Topic Models
In this paper we present a modification to a latent topic model, which m...
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Carl Henrik Ek
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Senior Lecturer in Computer Science at the University of Bristol, UK and a Docent in Machine Learning at the Royal Institute of Technology, Sweden.