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Diffusion-based data augmentation (DiffDA) has emerged as a promising approach to improving classification performance under data scarcity. However, existing works vary significantly in task configurations, model choices, and experimental…
Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according…
The research presented in this paper establishes a valid, and simplified, revision of previous spreadsheet error classifications. This investigation is concerned with the results of a web survey and two web-based gender and domain-knowledge…
Risk allocation, the decomposition of a portfolio-wide risk measure into component contributions, is a fundamental problem in financial risk management due to the non-additive nature of risk measures, the layered organizational structures…
We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they…
Realistic synthetic tabular data generation encounters significant challenges in preserving privacy, especially when dealing with sensitive information in domains like finance and healthcare. In this paper, we introduce \textit{Federated…
A business case study on how three simple guidelines: 1. Make it easy to check (and maintain) 2. Make it safe to use 3. Keep business logic out of code changed user attitudes and improved spreadsheet quality in a financial services…
There have been recent efforts to move to population-based structural health monitoring (PBSHM) systems. One area of PBSHM which has been recognised for potential development is the use of multi-task learning (MTL); algorithms which differ…
We develop a novel approach for the construction of quantile processes governing the stochastic dynamics of quantiles in continuous time. Two classes of quantile diffusions are identified: the first, which we largely focus on, features a…
We present Risk Integrated's Enterprise Spreadsheet Platform (ESP), a technical approach to the near-elimination of spreadsheet risk in the enterprise computing environment, whilst maintaining the full flexibility of spreadsheets for…
We study the difference between the level of systemic risk that is empirically measured on an interbank network and the risk that can be deduced from the balance sheets composition of the participating banks. Using generalised DebtRank…
Various techniques for developing spreadsheet models greatly improve the chance that the end result will not contain basic mechanical errors. However, for every discipline in which a given technique is useful, there is likely to be another…
The wealth of functionality in the Excel software package means it can go beyond use as a static evaluator of predefined cell formulae, to be used actively in manipulating and transforming data. Due to human error it is impossible to ensure…
Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell…
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in the future using their historical Electronic Health Records…
Context: Changing a software application with many build-time configuration settings may introduce unexpected side-effects. For example, a change intended to be specific to a platform (e.g., Windows) or product configuration (e.g.,…
Spreadsheet collaboration provides valuable opportunities for learning and expertise sharing between colleagues. Sharing expertise is essential for the retention of important technical skillsets within organisations, but previous studies…
In this study, we explore how to improve the functionality of multiclass classification algorithms. We used a benchmark dataset from Kaggle to create a framework. They have been used in a number of fields, including image recognition,…
While a substantial literature on structural break change point analysis exists for univariate time series, research on large panel data models has not been as extensive. In this paper, a novel method for estimating panel models with…
This paper proposes and compares measures of identity and attribute disclosure risk for synthetic data. Data custodians can use the methods proposed here to inform the decision as to whether to release synthetic versions of confidential…