12 Scaling Gaussian processes to large datasets

This chapter covers

  • Training a GP on a large dataset
  • Using mini-batch gradient descent when training a GP
  • Using an advanced gradient descent technique to train a GP faster

So far, we have seen that GPs offer great modeling flexibility. In chapter 3, we learned that we can model high-level trends using the GP’s mean function as well as variability using the covariance function. A GP also provides calibrated uncertainty quantification. That is, the predictions for datapoints near observations in the training dataset have lower uncertainty than those for points far away. This flexibility sets the GP apart from other ML models that produce only point estimates, such as neural networks. However, it ...

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