1 citations · 1 across the 3 of their papers we have counts for
7 papers
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data
W Yu, S Wade, H D Bondell +1
High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology,…
On MCMC for variationally sparse Gaussian processes: A pseudo-marginal approach
Karla Monterrubio-Gómez, Sara Wade
Gaussian processes (GPs) are frequently used in machine learning and statistics to construct powerful models. However, when employing GPs in practice, important considerations must…
Enriched Mixtures of Gaussian Process Experts
Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas
Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally. Combined with Gaussia…
Colombian Women's Life Patterns: A Multivariate Density Regression Approach
Sara Wade, Raffaella Piccarreta, Andrea Cremaschi +1
Women in Colombia face difficulties related to the patriarchal traits of their societies and well-known conflict afflicting the country since 1948. In this critical context, our ai…
Posterior Inference for Sparse Hierarchical Non-stationary Models
Karla Monterrubio-Gómez, Lassi Roininen, Sara Wade +2
Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this assumption can improve prediction…
Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models
Charles Gadd, Sara Wade, Akeel Shah +1
We introduce a Bayesian framework for inference with a supervised version of the Gaussian process latent variable model. The framework overcomes the high correlations between laten…