18 citations · 21 across the 6 of their papers we have counts for
6 papers
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
Conor Hassan, Joshua J Bon, Elizaveta Semenova +2
Federated learning (FL) allows for collaborative model training across decentralized clients while preserving privacy by avoiding data sharing. However, current FL methods assume c…
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
Conor Hassan, Matthew Sutton, Antonietta Mira +1
Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates. This paper introd…
Bayesian Cluster Geographically Weighted Regression for Spatial Heterogeneous Data
Wala Draidi Areed, Aiden Price, Helen Thompson +3
Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, spatial models and cluster algorithms can be co…
Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis
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. We specifically ou…
Federated Variational Inference Methods for Structured Latent Variable Models
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…
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Joshua J. Bon, Adam Bretherton, Katie Buchhorn +12
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most sta…