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Found 9 papers, 1 papers with code
Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process
Single-cell RNA sequencing (scRNA-seq) is powerful technology that allows researchers to understand gene expression patterns at the single-cell level.
Leveraging variational autoencoders for multiple data imputation
In this work, we investigate the ability of deep models, namely variational autoencoders (VAEs), to account for uncertainty in missing data through multiple imputation strategies.
Mixtures of Gaussian Process Experts with SMC$^2$
Gaussian processes are a key component of many flexible statistical and machine learning models.
Machine learning in the social and health sciences
The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, and research using ML for social and health research questions remains fragmented.
On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach
In complex models, the advantages of the PM scheme are particularly evident, and we demonstrate this on a two-level GP regression model with a nonparametric covariance function to capture non-stationarity.
Fast Deep Mixtures of Gaussian Process Experts
Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the entire density of the output to change with the inputs.
Enriched Mixtures of Gaussian Process Experts
Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally.
Posterior Inference for Sparse Hierarchical Non-stationary Models
Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed.
Computation
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models
We introduce a Bayesian framework for inference with a supervised version of the Gaussian process latent variable model.
