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The future of employment: How susceptible are jobs to computerisation?
We examine how susceptible jobs are to computerisation. To assess this, we begin by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, usingExpand
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Variational Inference for Gaussian Process Modulated Poisson Processes
TLDR
We present the first fully variational Bayesian inference scheme for continuous Gaussian-process-modulated Poisson processes that scales linearly in the number of observed events; and is many orders of magnitude faster than previous sampling based approaches. Expand
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Bayesian Gaussian processes for sequential prediction, optimisation and quadrature
We develop a family of Bayesian algorithms built around Gaussian processes for various problems posed by sensor networks. We firstly introduce an iterative Gaussian process for multi-sensor inferenceExpand
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Gaussian Processes for Global Optimization
We introduce a novel Bayesian approach to global optimization using Gaussian processes. We frame the optimization of both noisy and noiseless functions as sequential decision problems, and introduceExpand
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Probabilistic numerics and uncertainty in computations
TLDR
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Expand
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Active Learning of Model Evidence Using Bayesian Quadrature
TLDR
We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the marginal likelihood, predictive distribution, or normalising constant of a probabilistic model. Expand
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Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
TLDR
This paper establishes convergence rates for a new probabilistic approach to integration. Expand
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Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes
TLDR
In this paper, we describe a novel, computationally efficient algorithm that facilitates the autonomous acquisition of readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensors, and predicting how the monitored environmental variables will evolve into the future. Expand
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Probabilistic Integration: A Role in Statistical Computation?
TLDR
A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. Expand
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Probabilistic Integration: A Role for Statisticians in Numerical Analysis?
TLDR
A research frontier has emerged in scientific computation, founded on the principle that numerical error entails epistemic uncertainty that ought to be subjected to statistical analysis. Expand
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