
Zexun ChenUniversity of Edinburgh | UoE · Business School
Zexun Chen
Doctor of Philosophy
About
17
Publications
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Introduction
My previous research theoretically focused on Gaussian process and its extensions and I also have rich application experiences on analysing real financial time series using Gaussian process models and its extensions methods. Currently, I am a postdoc at University of Exeter and have strong interests in the positions closely related to probabilistic modelling research or its applications in financial time series.
Additional affiliations
October 2017 - December 2017
July 2013 - May 2016
Publications
Publications (17)
Gaussian process for vector-valued function model has been shown to be a useful method for multi-output prediction. The existing method for this model is to re-formulate the matrix-variate Gaussian distribution as a multivariate normal distribution. Although it is effective and convenient in many cases, re-formulation is not always workable and dif...
Gaussian Process Regression (GPR) is a kernel-based nonparametric method and has been proved to be effective and powerful. Its performance, however, relies on appropriate selection of kernel and the involving hyperparameters. The hyperparameters for a specified kernel are often estimated from the data via the maximum marginal likelihood. Unfortunat...
Human travelling behaviours are markedly regular, to a large extent predictable, and mostly driven by biological necessities and social constructs. Not surprisingly, such predictability is influenced by an array of factors ranging in scale from individual preferences and choices, through social groups and households, all the way to the global scale...
This paper introduces a novel framework, "peer-induced fairness", to scientifically audit algorithmic fairness. It addresses a critical but often overlooked issue: distinguishing between adverse outcomes due to algorithmic discrimination and those resulting from individuals' insufficient capabilities. By utilizing counterfactual fairness and advanc...
Human travelling behaviours are markedly regular, to a large extent, predictable, and mostly driven by biological necessities (e.g. sleeping, eating) and social constructs (e.g. school schedules, synchronisation of labour). Not surprisingly, such predictability is influenced by an array of factors ranging in scale from individual (e.g. preference,...
Gaussian processes occupy one of the leading places in modern statistics and probability theory due to their importance and a wealth of strong results. The common use of Gaussian processes is in connection with problems related to estimation, detection, and many statistical or machine learning models. In this paper, we propose a precise definition...
Social structures influence human behavior, including their movement patterns. Indeed, latent information about an individual’s movement can be present in the mobility patterns of both acquaintances and strangers. We develop a “colocation” network to distinguish the mobility patterns of an ego’s social ties from those not socially connected to the...
Spatio-temporal constraints coupled with social constructs have the potential to create fluid predictability to human mobility patterns. Accordingly, predictability in human mobility is non-monotonic and varies according to this spatio-socio-temporal context. Here, we propose that the predictability in human mobility is a {\em state} and not a stat...
The sensor-based structural health monitoring (SHM) systems are widely embedded in the new-constructed and rehabilitated dam. Due to the harsh working environment, poor installation, and sampling error, sensor fault often inevitably occurs. In this paper, rather than using conventional Gaussian process regression(GPR) to reconstruct missing data fr...
Social structures influence a variety of human behaviors including mobility patterns, but the extent to which one individual's movements can predict another's remains an open question. Further, latent information about an individual's mobility can be present in the mobility patterns of both social and non-social ties, a distinction that has not yet...
Structural health monitoring (SHM) is a powerful tool for identifying the underlying dam structural response anomalies by imitating the self-sensing ability of humans. Unfortunately, missing data often occur during the operation of the SHM system caused by various unfavorable factors, such as instrument failure, system downtime, and sensor aging. T...
Gaussian process occupies one of the leading places in modern Statistics and Probability due to its importance and wealth of results. The common use of GP is to connect with problems related to estimation, detection, and even many statistical or machine learning models. With the fast development of Gaussian process applications, it is necessary to...
Dam behavior prediction model is a fundamental component of dam structural health monitoring systems. As the most intuitive monitoring indicators, deformation is commonly used to reflect the dam behavior change. The selection of input variables and training samples determines the performance of dam deformation predictive models. In this paper, a no...
The original version of the acknowledgement unfortunately contained a mistake.
The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment of machine learning systems. We focus on mitigating the harm incurred by a biased machine learning system that offers better outputs (e.g., loans, job interviews) for cert...
Addressing fairness in machine learning models has recently attracted a lot of attention, as it will ensure continued confidence of the general public in the deployment of machine learning systems. Here, we focus on mitigating harm of a biased system that offers better outputs (e.g. loans, jobs) for certain groups than for others. We show that bias...
Gaussian process regression (GPR) is a kernel-based nonparametric method that has been proved to be effective and powerful in many areas, including time series prediction. In this thesis, we focus on GPR and its extensions and then apply them to financial time series prediction. We first review GPR, followed by a detailed discussion about model str...