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Top Papers in Gaussian processes

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A Generalized Kernel Model with Tractable Parameterization

Additive Gaussian Processes

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recycling independent variational approximations to processes

Recyclable Gaussian Processes

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Additive Gaussian Processes Revisited

Gaussian Process (GP) models are a class of flexible non-parametric models
that have rich representational power. By using a Gaussian process with
additive structure, complex responses can be modelled

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Bézier Curve Gaussian Processes

Probabilistic models for sequential data are the basis for a variety of
applications concerned with processing timely ordered information. The
predominant approach in this domain is given by neural ne

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Multi-group Gaussian Processes

Gaussian processes (GPs) are pervasive in functional data analysis, machine
learning, and spatial statistics for modeling complex dependencies. Modern
scientific data sets are typically heterogeneous

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Evolving-Graph Gaussian Processes

Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph
structured domains. Existing approaches have focused on static structures,
whereas many real graph data represent a dynamic s

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Probabilistic Bounds for Exact Process Models to Large Datasets

Adaptive Cholesky Gaussian Processes

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Automatic Forecasting using Gaussian Processes

Automatic forecasting is the task of receiving a time series and returning a
forecast for the next time steps without any human intervention. We propose an
approach for automatic forecasting based on

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Gaussian Processes for Survival Analysis

We introduce a semi-parametric Bayesian model for survival analysis. The
model is centred on a parametric baseline hazard, and uses a Gaussian process
to model variations away from it nonparametricall

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Incremental Ensemble Process Learning

Incremental Ensemble Gaussian Processes

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Learning Inconsistent Preferences with Gaussian Processes

We revisit widely used preferential Gaussian processes by Chu et al.(2005)
and challenge their modelling assumption that imposes rankability of data items
via latent utility function values. We propos

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Spatio-Temporal Variational Gaussian Processes

We introduce a scalable approach to Gaussian process inference that combines
spatio-temporal filtering with natural gradient variational inference,
resulting in a non-conjugate GP method for multivari

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Matern Gaussian Processes on Graphs

Gaussian processes are a versatile framework for learning unknown functions
in a manner that permits one to utilize prior information about their
properties. Although many different Gaussian process m

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An Intuitive Tutorial to Gaussian Processes Regression

This introduction aims to provide readers an intuitive understanding of
Gaussian processes regression. Gaussian processes regression (GPR) models have
been widely used in machine learning applications

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Variational Nearest Neighbor Gaussian Processes

Variational approximations to Gaussian processes (GPs) typically use a small
set of inducing points to form a low-rank approximation to the covariance
matrix. In this work, we instead exploit a sparse

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Orthogonally Decoupled Variational Gaussian Processes

Gaussian processes (GPs) provide a powerful non-parametric framework for
reasoning over functions. Despite appealing theory, its superlinear
computational and memory complexities have presented a long

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Gaussian Processes: A Quick Introduction

A gentle introduction to Gaussian processes (GPs). The three parts of the
document consider GPs for regression, classification, and dimensionality
reduction.

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A Bayesian formulation of deconditioning for multiresolution field downscaling

Deconditional Downscaling with Gaussian Processes

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Differential Privacy Protection for Bayesian Processes

Gaussian Processes with Differential Privacy

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Deep State-Space Gaussian Processes

This paper is concerned with a state-space approach to deep Gaussian process
(DGP) regression. We construct the DGP by hierarchically putting transformed
Gaussian process (GP) priors on the length sca

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