Sara Wade

According to our database, Sara Wade authored at least 13 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Fast deep mixtures of Gaussian process experts.

Clement Etienam, Kody J. H. Law, Sara Wade, Vitaly Zankin

Mach. Learn., 2024

2023

Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data.

Weichang Yu, Sara Wade, Howard D. Bondell, Lamiae Azizi

J. Comput. Graph. Stat., April, 2023

Leveraging Variational Autoencoders for Multiple Data Imputation.

Breeshey Roskams-Hieter, Jude Wells, Sara Wade

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022

Mixtures of Gaussian Process Experts with SMC².

Teemu Härkönen, Sara Wade, Kody J. H. Law, Lassi Roininen

CoRR, 2022

2021

Pseudo-marginal Bayesian inference for Gaussian process latent variable models.

Charles W. L. Gadd, Sara Wade, Akeel A. Shah

Mach. Learn., 2021

Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data.

Weichang Yu, Sara Wade, Howard D. Bondell, Lamiae Azizi

CoRR, 2021

Machine learning in the social and health sciences.

Anja K. Leist, Matthias Klee, Jung Hyun Kim, David H. Rehkopf, Stéphane P. A. Bordas, Graciela Muniz-Terrera, Sara Wade

CoRR, 2021

2020

Posterior inference for sparse hierarchical non-stationary models.

Karla Monterrubio-Gómez, Lassi Roininen, Sara Wade, Theodoros Damoulas, Mark Girolami

Comput. Stat. Data Anal., 2020

Ultra-fast Deep Mixtures of Gaussian Process Experts.

Clement Etienam, Kody J. H. Law, Sara Wade

CoRR, 2020

Enriched mixtures of generalised Gaussian process experts.

Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Enriched Mixtures of Gaussian Process Experts.

Charles W. L. Gadd, Sara Wade, Alexis Boukouvalas

CoRR, 2019

2018

Pseudo-marginal Bayesian inference for supervised Gaussian process latent variable models.

Charles W. L. Gadd, Sara Wade, Akeel A. Shah, Dimitris Grammatopoulos

CoRR, 2018

2014

Improving prediction from dirichlet process mixtures via enrichment.

Sara Wade, David B. Dunson, Sonia Petrone, Lorenzo Trippa

J. Mach. Learn. Res., 2014