| Sarah Wade |
| Tue 07 May 2019, 11:00 - 12:00 |
| IF 4.31/4.33 |
If you have a question about this talk, please contact: Gareth Beedham (gbeedham)
Title: Enriched mixtures of generalised Gaussian process experts
Abstract: 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 Gaussian process (GP) experts, this results in a powerful and highly flexible model. We focus on alternative mixtures of GP experts, which model the joint distribution of the inputs and targets explicitly. We highlight issues of this approach in multi-dimensional input spaces, namely, poor scalability and the need for an unnecessarily large number of experts, degrading the predictive performance and increasing uncertainty. We construct a novel model to address these issues through a nested partitioning scheme that automatically infers the number of components at both levels. Multiple response types are accommodated through a generalised GP framework, while multiple input types are included through a factorised exponential family structure. We show the effectiveness of our approach in estimating a parsimonious probabilistic description of both synthetic data of increasing dimension and an Alzheimer's challenge dataset to predict decline in cognitive impairment.
Short Bio:
Dr Sara Wade joined the School of Mathematics, University of Edinburgh, as a Lecturer in Statistics and Data Science in October 2018. Before this, she was an Assistant Professor in Statistics at the University of Warwick and a Postdoctoral Researcher in Machine Learning at the University of Cambridge working with Prof. Zoubin Ghahramani. She earned her PhD in Statistics from Bocconi University, under the supervision of Prof. Sonia Petrone and Prof. Stephen Walker. Her research covers Bayesian nonparametrics and machine learning, with an emphasis on the development of flexible nonparametric priors and efficient inference for complex data.