84 citations · 134 across the 15 of their papers we have counts for
16 papers
How does academic performance affect self-efficacy? Interpretable modelling through latent academic achievement
Sarah Lee, Matias Quiroz, Sally Cripps
There is increasing evidence of a directional relationship from academic performance to self-efficacy. We develop a Bayesian model for investigating this relationship when academic…
Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning
Yun Li, Zhe Liu, Saurav Jha +2
Open-World Compositional Zero-Shot Learning (OW-CZSL) aims to recognize new compositions of seen attributes and objects. In OW-CZSL, methods built on the conventional closed-world…
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation
Siyu Wang, Xiaocong Chen, Lina Yao +2
Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. How…
A Case Study in Model Failure? COVID-19 Daily Deaths and ICU Bed Utilisation Predictions in New York State
Vincent Chin, Noelle I. Samia, Roman Marchant +4
Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we di…
Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions
Roman Marchant, Noelle I. Samia, Ori Rosen +2
This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for p…
AdaptSPEC-X: Covariate Dependent Spectral Modeling of Multiple Nonstationary Time Series
Michael Bertolacci, Ori Rosen, Edward Cripps +1
We present a method for the joint analysis of a panel of possibly nonstationary time series. The approach is Bayesian and uses a covariate-dependent infinite mixture model to incor…
Structured Variational Inference in Continuous Cox Process Models
Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas +1
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is…
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series
Nick James, Roman Marchant, Richard Gerlach +1
Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series. This paper uses an adaptive spectral te…
Efficiency and robustness in Monte Carlo sampling of 3-D geophysical inversions with Obsidian v0.1.2: Setting up for success
Richard Scalzo, David Kohn, Hugo Olierook +4
The rigorous quantification of uncertainty in geophysical inversions is a challenging problem. Inversions are often ill-posed and the likelihood surface may be multi-modal; propert…
Langevin-gradient parallel tempering for Bayesian neural learning
Rohitash Chandra, Konark Jain, Ratneel V. Deo +1
Bayesian neural learning feature a rigorous approach to estimation and uncertainty quantification via the posterior distribution of weights that represent knowledge of the neural n…
Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics
Jodie Pall, Rohitash Chandra, Danial Azam +4
Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic…
Multi-core parallel tempering Bayeslands for basin and landscape evolution
Rohitash Chandra, R. Dietmar Müller, Danial Azam +4
The Bayesian paradigm is becoming an increasingly popular framework for estimation and uncertainty quantification of unknown parameters in geo-physical inversion problems. Badlands…
Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands
Rohitash Chandra, Danial Azam, R. Dietmar Müller +2
Bayesian inference provides a rigorous methodology for estimation and uncertainty quantification of parameters in geophysical forward models. Badlands (basin and landscape dynamics…
A comparison of methods for modeling marginal non-zero daily rainfall across the Australian continent
Michael Bertolacci, Edward Cripps, Ori Rosen +1
Naveau et al. (2016) have recently developed a class of methods, based on extreme-value theory (EVT), for capturing low, moderate, and heavy rainfall simultaneously, without the ne…
Irreproducibility; Nothing is More Predictable
David Kohn, Nick Glozier, Ian B. Hickie +2
The increasing ease of data capture and storage has led to a corresponding increase in the choice of data, the type of analysis performed on that data, and the complexity of the an…
A Bayesian Approach to Predicting Disengaged Youth
David Kohn, Sally Cripps, Nick Glozier +1
This article presents a Bayesian approach for predicting and identifying the factors which most influence an individual's propensity to fall into the category of Not in Employment…