Search Results for author:
Found 19 papers, 5 papers with code
Kernel Identification Through Transformers
Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of functions under the GP prior.
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.
The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain
In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions.
A Tutorial on Sparse Gaussian Processes and Variational Inference
In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood.
Matérn 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.
Sparse Gaussian Processes with Spherical Harmonic Features
We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations.
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation
Many machine learning models require a training procedure based on running stochastic gradient descent.
Doubly Sparse Variational Gaussian Processes
The use of Gaussian process models is typically limited to datasets with a few tens of thousands of observations due to their complexity and memory footprint.
Bayesian Quantile and Expectile Optimisation
In this paper, we propose new variational models for Bayesian quantile and expectile regression that are well-suited for heteroscedastic noise settings.
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
We introduce a novel finite approximation of GP-modulated Cox processes where positiveness conditions can be imposed directly on the GP, with no restrictions on the covariance function.
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
Banded matrices can be used as precision matrices in several models including linear state-space models, some Gaussian processes, and Gaussian Markov random fields.
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Finally, on 2D and 5D coastal flooding applications, we show that more flexible and realistic GP implementations can be obtained by considering noise effects and by enforcing the (linear) inequality constraints.
Scalable GAM using sparse variational Gaussian processes
Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linear models.
Physically-Inspired Gaussian Process Models for Post-Transcriptional Regulation in Drosophila
Since the post-transcriptional regulation of Drosophila depends on spatiotemporal interactions between mRNAs and gap proteins, proper physically-inspired stochastic models are required to study the link between both quantities.
Finite-dimensional Gaussian approximation with linear inequality constraints
Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems.
Variational Fourier features for Gaussian processes
This work brings together two powerful concepts in Gaussian processes: the variational approach to sparse approximation and the spectral representation of Gaussian processes.
Nested Kriging predictions for datasets with large number of observations
This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function.
An analytic comparison of regularization methods for Gaussian Processes
A measure for data-model discrepancy is proposed which serves for choosing a regularization technique. In the second part of the paper, a distribution-wise GP is introduced that interpolates Gaussian distributions instead of data points.
Invariances of random fields paths, with applications in Gaussian Process Regression
We study pathwise invariances of centred random fields that can be controlled through the covariance.
