QUT
Bayesian hierarchical stacking—All models are wrong, but some are somewhere useful
Bayesian hierarchical stacking—All models are wrong, but some are somewhere useful Stacking is a widely used model averaging technique. Like many other ensemble methods, stacking is more effective when model predictive performance is heterogeneous in inputs, in which case we can further improve the stacked mixture with a hierarchical model. In this talk I will focus on the recent development of Bayesian…
Data Science Under the Hood: Manifold Learning
Data Science Under the Hood: Manifold Learning This talk introduces Manifold Learning, the technique to uncover the intrinsic shape of the original data. We also discuss how different manifold learning paradigms can be designed to be incorporated to a dimensionality reduction technique to learn the accurate low-dimensional data representation. Register now
Distinguished Visitor Seminar – Assistant Professor Linda Tan
Distinguished Visitor Seminar - Assistant Professor Linda Tan The Centre for Data Science Distinguished Visitor Seminar Series has been created to provide a platform for our esteemed colleagues from across the nation and the globe to share their research, knowledge and expertise in the field of data science. Register now The next seminar will be presented by Linda…