M
Michael A. Osborne
Researcher at University of Oxford
Publications - 186
Citations - 10823
Michael A. Osborne is an academic researcher from University of Oxford. The author has contributed to research in topics: Gaussian process & Bayesian probability. The author has an hindex of 34, co-authored 167 publications receiving 8439 citations. Previous affiliations of Michael A. Osborne include Australian Nuclear Science and Technology Organisation & University of Southampton.
Papers
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The future of employment: How susceptible are jobs to computerisation?
TL;DR: In this paper, a Gaussian process classifier was used to estimate the probability of computerisation for 702 detailed occupations, and the expected impacts of future computerisation on US labour market outcomes, with the primary objective of analyzing the number of jobs at risk and the relationship between an occupations probability of computing, wages and educational attainment.
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Gaussian processes for time-series modelling
Stephen J. Roberts,Michael A. Osborne,Mark Ebden,Steve Reece,Neale P. Gibson,Suzanne Aigrain +5 more
TL;DR: This paper discusses how domain knowledge influences design of the Gaussian process models and provides case examples to highlight the approaches.
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Gaussian process regression for forecasting battery state of health
TL;DR: This work proposes Gaussian process (GP) regression for forecasting battery state of health, and highlights various advantages of GPs over other data-driven and mechanistic approaches.
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A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy
Neale P. Gibson,Suzanne Aigrain,Steve G. Roberts,Thomas M. Evans,Michael A. Osborne,Frederic Pont +5 more
TL;DR: In this paper, the authors proposed a nonparametric Gaussian Process (GP) method to infer transit parameters in the presence of systematic noise using Gaussian processes, a technique widely used in the machine learning community for Bayesian regression and classification problems.
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A Gaussian process framework for modelling stellar activity signals in radial velocity data
TL;DR: A GP framework is presented to model RV time series jointly with ancillary activity indicators, allowing the activity component of RV timeseries to be constrained and disentangled from e.g. planetary components.