no code implementations • 25 May 2024 • Conor Hassan, Joshua J Bon, Elizaveta Semenova, Antonietta Mira, Kerrie Mengersen
We demonstrate the SIGMA prior's effectiveness on synthetic data and showcase its utility in a real-world example of FL for spatial data, using a conditional autoregressive prior to model spatial dependence across Australia.
no code implementations • 7 May 2024 • Conor Hassan, Matthew Sutton, Antonietta Mira, Kerrie Mengersen
Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates.
no code implementations • 8 Aug 2023 • Paul Pao-Yen Wu, Fabrizio Ruggeri, Kerrie Mengersen
A Directed Acyclic Graph (DAG) can be partitioned or mapped into clusters to support and make inference more computationally efficient in Bayesian Network (BN), Markov process and other models.
no code implementations • 28 Jul 2023 • Conor Hassan, Robert Salomone, Kerrie Mengersen
This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets.
no code implementations • 25 May 2023 • wala Draidi Areed, Aiden Price, Kathryn Arnett, Helen Thompson, Reid Malseed, Kerrie Mengersen
The research explores the influence of preschool attendance (one year before full-time school) on the development of children during their first year of school.
1 code implementation • 19 Apr 2023 • Katie Buchhorn, Edgar Santos-Fernandez, Kerrie Mengersen, Robert Salomone
We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data.
2 code implementations • 17 Feb 2023 • Ethan Goan, Dimitri Perrin, Kerrie Mengersen, Clinton Fookes
Inference on modern Bayesian Neural Networks (BNNs) often relies on a variational inference treatment, imposing violated assumptions of independence and the form of the posterior.
no code implementations • 7 Feb 2023 • Conor Hassan, Robert Salomone, Kerrie Mengersen
Federated learning methods enable model training across distributed data sources without data leaving their original locations and have gained increasing interest in various fields.
no code implementations • 18 Jan 2023 • Hong-Bo Xie, Caoyuan Li, Shuliang Wang, Richard Yi Da Xu, Kerrie Mengersen
Construction of dictionaries using nonnegative matrix factorisation (NMF) has extensive applications in signal processing and machine learning.
2 code implementations • 9 Sep 2022 • Owen Forbes, Edgar Santos-Fernandez, Paul Pao-Yen Wu, Hong-Bo Xie, Paul E. Schwenn, Jim Lagopoulos, Lia Mills, Dashiell D. Sacks, Daniel F. Hermens, Kerrie Mengersen
In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms.
no code implementations • 8 Mar 2022 • Abhishek Varghese, Edgar Santos-Fernandez, Francesco Denti, Antonietta Mira, Kerrie Mengersen
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a country's stringency of lockdown policies.
1 code implementation • 16 Mar 2020 • Edgar Santos-Fernandez, Kerrie Mengersen
We introduce a new methodological framework of item response and linear logistic test models with application to citizen science data used in ecology research.
Applications
2 code implementations • 27 Jun 2017 • Benjamin R. Fitzpatrick, Kerrie Mengersen
Together these visualisations facilitate substantial insights into the roles of covariates in a random forest but do not communicate the frequencies of the hierarchies of covariates effects across the random forest or the orders in which covariates occur in these hierarchies.
Other Statistics
1 code implementation • 6 Mar 2017 • Anthony Ebert, Paul Wu, Kerrie Mengersen, Fabrizio Ruggeri
Approximate Bayesian computation could offer a straight-forward way to infer parameters for such networks if we could simulate data quickly enough.
Computation Optimization and Control