Publications (11)
Anomaly Detection and Removal Using Non-Stationary Gaussian Processes
Steven Reece, Roman Garnett, Michael Osborne +1
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault fr…
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning
Thomas Nickson, Michael A Osborne, Steven Reece +1
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters…
Efficient State-Space Inference of Periodic Latent Force Models
Steven Reece, Stephen Roberts, Siddhartha Ghosh +2
Latent force models (LFM) are principled approaches to incorporating solutions to differential equations within non-parametric inference methods. Unfortunately, the development and…
Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks
Steven Reece, Emma O'Donnell, Felicia Liu +4
There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating…
Disaster mapping from satellites: damage detection with crowdsourced point labels
Danil Kuzin, Olga Isupova, Brooke D. Simmons +1
High-resolution satellite imagery available immediately after disaster events is crucial for response planning as it facilitates broad situational awareness of critical infrastruct…
Bayesian Heatmaps: Probabilistic Classification with Multiple Unreliable Information Sources
Edwin Simpson, Steven Reece, Stephen J. Roberts
Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these diff…