
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Deep Gaussian processes (DGPs) have struggled for relevance in applicati...
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GPflux: A Library for Deep Gaussian Processes
We introduce GPflux, a Python library for Bayesian deep learning with a ...
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Sparse Gaussian Processes with Spherical Harmonic Features
We introduce a new class of interdomain variational Gaussian processes ...
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Amortized variance reduction for doubly stochastic objectives
Approximate inference in complex probabilistic models such as deep Gauss...
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A Framework for Interdomain and Multioutput Gaussian Processes
One obstacle to the use of Gaussian processes (GPs) in largescale probl...
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Doubly Sparse Variational Gaussian Processes
The use of Gaussian process models is typically limited to datasets with...
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Overcoming MeanField Approximations in Recurrent Gaussian Process Models
We identify a new variational inference scheme for dynamical systems who...
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Deep Gaussian Processes with ImportanceWeighted Variational Inference
Deep Gaussian processes (DGPs) can model complex marginal densities as w...
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Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Banded matrices can be used as precision matrices in several models incl...
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Translation Insensitivity for Deep Convolutional Gaussian Processes
Deep learning has been at the foundation of large improvements in image ...
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NonFactorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discret...
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InfiniteHorizon Gaussian Processes
Gaussian processes provide a flexible framework for forecasting, removin...
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Gaussian Process Conditional Density Estimation
Conditional Density Estimation (CDE) models deal with estimating conditi...
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Learning Invariances using the Marginal Likelihood
Generalising well in supervised learning tasks relies on correctly extra...
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LargeScale Cox Process Inference using Variational Fourier Features
Gaussian process modulated Poisson processes provide a flexible framewor...
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Natural Gradients in Practice: NonConjugate Variational Inference in Gaussian Process Models
The natural gradient method has been used effectively in conjugate Gauss...
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Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into G...
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Pseudoextended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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Scalable Joint Models for Reliable UncertaintyAware Event Prediction
Missing data and noisy observations pose significant challenges for reli...
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Identification of Gaussian Process State Space Models
The Gaussian process state space model (GPSSM) is a nonlinear dynamical...
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Variational Fourier features for Gaussian processes
This work brings together two powerful concepts in Gaussian processes: t...
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GPflow: A Gaussian process library using TensorFlow
GPflow is a Gaussian process library that uses TensorFlow for its core c...
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Chained Gaussian Processes
Gaussian process models are flexible, Bayesian nonparametric approaches...
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MCMC for Variationally Sparse Gaussian Processes
Gaussian process (GP) models form a core part of probabilistic machine l...
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Spike and Slab Gaussian Process Latent Variable Models
The Gaussian process latent variable model (GPLVM) is a popular approac...
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On Sparse variational methods and the KullbackLeibler divergence between stochastic processes
The variational framework for learning inducing variables (Titsias, 2009...
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Nested Variational Compression in Deep Gaussian Processes
Deep Gaussian processes provide a flexible approach to probabilistic mod...
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Scalable Variational Gaussian Process Classification
Gaussian process classification is a popular method with a number of app...
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Gaussian Process Models with Parallelization and GPU acceleration
In this work, we present an extension of Gaussian process (GP) models wi...
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Fast nonparametric clustering of structured timeseries
In this publication, we combine two Bayesian nonparametric models: the ...
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Gaussian Processes for Big Data
We introduce stochastic variational inference for Gaussian process model...
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Fast Variational Inference in the Conjugate Exponential Family
We present a general method for deriving collapsed variational inference...
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James Hensman
verfied profile
Research team lead at PROWLER.IO