18 citations · 23 across the 8 of their papers we have counts for
8 papers
A PAC-Bayesian Perspective on the Interpolating Information Criterion
Liam Hodgkinson, Chris van der Heide, Robert Salomone +2
Deep learning is renowned for its theory-practice gap, whereby principled theory typically fails to provide much beneficial guidance for implementation in practice. This has been h…
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…
The Interpolating Information Criterion for Overparameterized Models
Liam Hodgkinson, Chris van der Heide, Robert Salomone +2
The problem of model selection is considered for the setting of interpolating estimators, where the number of model parameters exceeds the size of the dataset. Classical informatio…
Graph Neural Network-Based Anomaly Detection for River Network Systems
Katie Buchhorn, Edgar Santos-Fernandez, Kerrie Mengersen +1
Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is i…
Structured variational approximations with skew normal decomposable graphical models
Robert Salomone, Xuejun Yu, David J. Nott +1
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred…
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…
Transport Reversible Jump Proposals
Laurence Davies, Robert Salomone, Matthew Sutton +1
Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications. Inspired b…