Carl Henrik Ek

research

∙ 10/29/2021

Aligned Multi-Task Gaussian Process

Multi-task learning requires accurate identification of the correlations...

Olga Mikheeva, et al. ∙

research

∙ 05/10/2021

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Deep Gaussian processes (DGPs) have struggled for relevance in applicati...

Vincent Dutordoir, et al. ∙

research

∙ 10/26/2020

Black-box density function estimation using recursive partitioning

We present a novel approach to Bayesian inference and general Bayesian c...

Erik Bodin, et al. ∙

research

∙ 01/27/2020

Bayesian nonparametric shared multi-sequence time series segmentation

In this paper, we introduce a method for segmenting time series data usi...

Olga Mikheeva, et al. ∙

research

∙ 09/17/2019

Compositional uncertainty in deep Gaussian processes

Gaussian processes (GPs) are nonparametric priors over functions, and fi...

Ivan Ustyuzhaninov, et al. ∙

research

∙ 07/10/2019

Interpretable Dynamics Models for Data-Efficient Reinforcement Learning

In this paper, we present a Bayesian view on model-based reinforcement l...

Markus Kaiser, et al. ∙

research

∙ 06/26/2019

Modulated Bayesian Optimization using Latent Gaussian Process Models

We present an approach to Bayesian Optimization that allows for robust s...

Erik Bodin, et al. ∙

research

∙ 05/30/2019

Monotonic Gaussian Process Flow

We propose a new framework of imposing monotonicity constraints in a Bay...

Ivan Ustyuzhaninov, et al. ∙

research

∙ 01/30/2019

Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning

This paper presents an approach for learning invariant features for obje...

Martin Hjelm, et al. ∙

research

∙ 12/13/2018

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...

Alessandro Di Martino, et al. ∙

research

∙ 11/26/2018

Sequence Alignment with Dirichlet Process Mixtures

We present a probabilistic model for unsupervised alignment of high-dime...

Ieva Kazlauskaite, et al. ∙

research

∙ 10/16/2018

Multimodal Deep Gaussian Processes

We propose a novel Bayesian approach to modelling multimodal data genera...

Markus Kaiser, et al. ∙

research

∙ 07/12/2018

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...

Andrew R. Lawrence, et al. ∙

research

∙ 05/18/2018

Model Inference with Stein Density Ratio Estimation

The Kullback-Leilber divergence from model to data is a classic goodness...

Song Liu, et al. ∙

research

∙ 03/07/2018

Gaussian Process Latent Variable Alignment Learning

We present a model that can automatically learn alignments between high-...

Ieva Kazlauskaite, et al. ∙

research

∙ 02/13/2018

Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces

In this work we study the problem of exploring surfaces and building com...

Sergio Caccamo, et al. ∙

research

∙ 12/18/2017

Nonparametric Inference for Auto-Encoding Variational Bayes

We would like to learn latent representations that are low-dimensional a...

Erik Bodin, et al. ∙

research

∙ 10/08/2017

Bayesian Alignments of Warped Multi-Output Gaussian Processes

We present a Bayesian extension to convolution processes which defines a...

Markus Kaiser, et al. ∙

research

∙ 08/11/2017

Neural Translation of Musical Style

Music is an expressive form of communication often used to convey emotio...

Iman Malik, et al. ∙

research

∙ 07/18/2017

Latent Gaussian Process Regression

We introduce Latent Gaussian Process Regression which is a latent variab...

Erik Bodin, et al. ∙

research

∙ 01/12/2017

Manifold Alignment Determination: finding correspondences across different data views

We present Manifold Alignment Determination (MAD), an algorithm for lear...

Andreas Damianou, et al. ∙

research

∙ 06/30/2016

Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model

Unsupervised learning on imbalanced data is challenging because, when gi...

Fariba Yousefi, et al. ∙

research

∙ 05/19/2016

Inter-Battery Topic Representation Learning

In this paper, we present the Inter-Battery Topic Model (IBTM). Our appr...

Cheng Zhang, et al. ∙

research

∙ 04/17/2016

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

Factor analysis aims to determine latent factors, or traits, which summa...

Andreas Damianou, et al. ∙

research

∙ 11/24/2014

Persistent Evidence of Local Image Properties in Generic ConvNets

Supervised training of a convolutional network for object classification...

Ali Sharif Razavian, et al. ∙

research

∙ 01/15/2013

Factorized Topic Models

In this paper we present a modification to a latent topic model, which m...

Cheng Zhang, et al. ∙

Carl Henrik Ek

Featured Co-authors

Zoubin Ghahramani

Cheng Zhang

Danica Kragic

Neil D. Lawrence

Hedvig Kjellström

Mark van der Wilk

James Hensman

Wittawat Jitkrittum

Song Liu

Nicolas Durrande

Andreas Damianou

Aligned Multi-Task Gaussian Process

Deep Neural Networks as Point Estimates for Deep Gaussian Processes

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