We consider the problem of sequential estimation of the unknowns of state-space and deep
state-space models that include estimation of functions and latent processes of the models …

Gaussian processes (GPs) are a powerful and useful approach for modelling nonlinear
phenomena in various scientific fields, including genomics and genetics. This review …

Gaussian processes are widely used for the analysis of spatial data due to their
nonparametric flexibility and ability to quantify uncertainty, and recently developed scalable …

In this paper, we address the problem of sequential processing of observations modeled by
deep Gaussian process state space models. First, we introduce the model where the Gaus …

Gaussian process latent variable models (GPLVMs) are a versatile family of unsupervised
learning models commonly used for dimensionality reduction. However, common …

The Gaussian process latent variable model (GPLVM) is a popular probabilistic method
used for nonlinear dimension reduction, matrix factorization, and state-space modeling …

One of the most important problems in science is understanding causation. This is
particularly challenging when one has access to observational data only and is further …

Random feature latent variable models (RFLVMs) represent the state-of-the-art in latent
variable models, capable of handling non-Gaussian likelihoods and effectively uncovering …

In this paper, we address the classification and dimensionality reduction via ensembles of
Gaussian Process Latent Variable Models (GPLVMs). The underlying idea is to have a …

The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified
representation from multi-view data but is hindered by challenges such as limited kernel …