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04/15/2023
Dimensionality Reduction as Probabilistic Inference
Dimensionality reduction (DR) algorithms compress high-dimensional data ...
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11/04/2022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
The kernel function and its hyperparameters are the central model select...
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09/14/2022
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
Single-cell RNA-seq datasets are growing in size and complexity, enablin...
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09/11/2022
Kernel Learning for Explainable Climate Science
The Upper Indus Basin, Himalayas provides water for 270 million people a...
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02/25/2022
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference
Gaussian process latent variable models (GPLVM) are a flexible and non-l...
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06/15/2021
Kernel Identification Through Transformers
Kernel selection plays a central role in determining the performance of ...
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10/30/2020
Marginalised Gaussian Processes with Nested Sampling
Gaussian Process (GPs) models are a rich distribution over functions wit...
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01/19/2020
A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application
The aim of this work is to propose a meta-algorithm for automatic classi...
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01/16/2020
Extracting more from boosted decision trees: A high energy physics case study
Particle identification is one of the core tasks in the data analysis pi...
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12/31/2019
Approximate Inference for Fully Bayesian Gaussian Process Regression
Learning in Gaussian Process models occurs through the adaptation of hyp...
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11/17/2018