Gaussian process

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model2 events

papersTODAY 04:00 UTC

Bayesian Optimisation Method Combines Expert Gaussian Processes With Calibrated Uncertainty

A new arXiv preprint proposes using a product-of-experts Gaussian process as the surrogate model in Bayesian optimisation, rather than a single global GP. The authors address the cubic scaling cost of standard GP regression with training set size, which restricts its use on larger datasets, and add a calibration step for uncertainty estimates. The work falls in the machine learning methodology category and has not yet been peer reviewed.

bayesian-optimisation/gaussian-processes/uncertainty/arxiv

papersSEP 11 04:00 UTC

arXiv paper targets lower-tail calibration of Gaussian processes for Bayesian optimization

An updated arXiv preprint proposes a goal-oriented approach to calibrating the lower tail of Gaussian process predictive distributions, which Bayesian optimization uses to choose where to evaluate costly objective functions. The abstract notes that kernel and hyperparameter choices strongly shape these predictions. The submission is a replacement version (v2) of an earlier preprint.

bayesian-optimization/gaussian-processes/calibration/arxiv