Anders Uhrenholt

a.uhrenholt.1@research.gla.ac.uk

Research title: Efficient target vector identification and Bayesian complexity reduction in Gaussian processes (tentative)

Research Summary

My research centers on machine learning in scenarios where only limited data are available, e.g. patient specific modelling, online experimental design, and user interaction. More broadly, I am interested in problems that require us, as machine learning practioners, to

  1. make the most the available data and computational resources, and
  2. robustly quantify our model accuracy and uncertainty.

The main methodologies in my work are Gaussian processes and Bayesian optimisation.

Publications

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Jump to: 2021
Number of items: 1.

2021

Uhrenholt, A. K. , Charvet, V. and Jensen, B. S. (2021) Probabilistic Selection of Inducing Points in Sparse Gaussian Processes. In: 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021), 27-29 Jul 2021, (Accepted for Publication)

This list was generated on Mon Jul 26 12:46:02 2021 BST.
Number of items: 1.

Conference Proceedings

Uhrenholt, A. K. , Charvet, V. and Jensen, B. S. (2021) Probabilistic Selection of Inducing Points in Sparse Gaussian Processes. In: 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021), 27-29 Jul 2021, (Accepted for Publication)

This list was generated on Mon Jul 26 12:46:02 2021 BST.