175 citations · 365 across the 13 of their papers we have counts for
15 papers
The generalized underlap coefficient with an application in clustering
Zhaoxi Zhang, Vanda Inácio, Sara Wade
Quantifying distributional separation across groups is fundamental in statistical learning and scientific discovery, yet most classical discrepancy measures are tailored to two-gro…
Bayesian dependent mixture models: A predictive comparison and survey
Sara Wade, Vanda Inacio, Sonia Petrone
For exchangeable data, mixture models are an extremely useful tool for density estimation due to their attractive balance between smoothness and flexibility. When additional covari…
Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process
Jinlu Liu, Sara Wade, Natalia Bochkina
Single-cell RNA sequencing (scRNA-seq) is powerful technology that allows researchers to understand gene expression patterns at the single-cell level. However, analysing scRNA-seq…
Leveraging variational autoencoders for multiple data imputation
Breeshey Roskams-Hieter, Jude Wells, Sara Wade
Missing data persists as a major barrier to data analysis across numerous applications. Recently, deep generative models have been used for imputation of missing data, motivated by…
Mixtures of Gaussian Process Experts with SMC
Teemu Härkönen, Sara Wade, Kody Law +1
Gaussian processes are a key component of many flexible statistical and machine learning models. However, they exhibit cubic computational complexity and high memory constraints du…
Bayesian nonparametric scalar-on-image regression via Potts-Gibbs random partition models
Mica Teo Shu Xian, Sara Wade
Scalar-on-image regression aims to investigate changes in a scalar response of interest based on high-dimensional imaging data. We propose a novel Bayesian nonparametric scalar-on-…
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…
Fast Deep Mixtures of Gaussian Process Experts
Clement Etienam, Kody Law, Sara Wade +1
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 out…
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…
Discussion on Bayesian Cluster Analysis: Point Estimation and Credible Balls by Sara Wade and Zoubin Ghahramani
William Weimin Yoo
I begin my discussion by giving an overview of the main results. Then I proceed to touch upon issues about whether the credible ball constructed can be interpreted as a confidence…
Bayesian cluster analysis: Point estimation and credible balls
Sara Wade, Zoubin Ghahramani
Clustering is widely studied in statistics and machine learning, with applications in a variety of fields. As opposed to classical algorithms which return a single clustering solut…