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Gaussian Processes
In the previous chapter, we covered the derivation of the posterior distribution for parameter θ as well as the predictive posterior distribution of...
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Heterogeneous multi-task Gaussian Cox processes
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g.,...
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Large scale multi-output multi-class classification using Gaussian processes
Multi-output Gaussian processes (MOGPs) can help to improve predictive performance for some output variables, by leveraging the correlation with...
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Facial Deepfake Detection Using Gaussian Processes
Facial deepfake detection involves detecting images and videos with tampered faces. In this paper, we automatically detect four types of deepfakes:...
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Gaussian processes for Bayesian inverse problems associated with linear partial differential equations
This work is concerned with the use of Gaussian surrogate models for Bayesian inverse problems associated with linear partial differential equations....
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Scalable computations for nonstationary Gaussian processes
Nonstationary Gaussian process models can capture complex spatially varying dependence structures in spatial data. However, the large number of...
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Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming
Gaussian processes are powerful non-parametric probabilistic models for stochastic functions. However, the direct implementation entails a complexity...
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Efficient reduced-rank methods for Gaussian processes with eigenfunction expansions
In this work, we introduce a reduced-rank algorithm for Gaussian process regression. Our numerical scheme converts a Gaussian process on a...
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A non-stationary model for spatially dependent circular response data based on wrapped Gaussian processes
Circular data can be found across many areas of science, for instance meteorology (e.g., wind directions), ecology (e.g., animal movement...
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Active Learning with Weak Supervision for Gaussian Processes
Annotating data for supervised learning can be costly. When the annotation budget is limited, active learning can be used to select and annotate...
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Novel approaches for hyper-parameter tuning of physics-informed Gaussian processes: application to parametric PDEs
Today, Physics-informed machine learning (PIML) methods are one of the effective tools with high flexibility for solving inverse problems and...
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Bayesian Uncertainty Estimation in Landmark Localization Using Convolutional Gaussian Processes
Landmark localization is an important step in image analysis, where the clinical definition of a landmark can be ambiguous, leading to a practical...
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Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series
The classification of irregularly sampled Satellite image time-series (SITS) is investigated in this paper. A multivariate Gaussian process mixture...
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Learning with deep Gaussian processes and homothety in weather simulation
Observations and numerical prediction models are the main methods for measuring and estimating the earth's energy balance parameters. However, the...
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Automated Model Inference for Gaussian Processes: An Overview of State-of-the-Art Methods and Algorithms
Gaussian process models (GPMs) are widely regarded as a prominent tool for learning statistical data models that enable interpolation, regression,...
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Manifold learning by a deep Gaussian process autoencoder
The paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian...
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Point process simulation of generalised inverse Gaussian processes and estimation of the Jaeger integral
In this paper novel simulation methods are provided for the generalised inverse Gaussian (GIG) Lévy process. Such processes are intractable for...
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MAGMA: inference and prediction using multi-task Gaussian processes with common mean
A novel multi-task Gaussian process (GP) framework is proposed, by using a common mean process for sharing information across tasks. In particular,...
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Dynamically Self-adjusting Gaussian Processes for Data Stream Modelling
One of the major challenges in time series analysis are changing data distributions, especially when processing data streams. To ensure an up-to-date...