Incorporating additional data to train gp_model

Hi,

I have a few questions regarding the incorporation of additional data to train the existing gp_model. As GP is a nonparametric algorithm which can learn as the data grows. Is there a way to incorporate additional data in gpytorch to update the hyperparameters without recalculating again using the whole data. I found two methods in GPyTorch which seems to be related to that :

get_fantasy_data() - problem with this is that the method is not available for multitask GPs set_train_data() My query is regarding the second method; in documentation, it is given that the function doesn’t refit the hyperparameter. Does that mean it doesn’t update the hyperparameters at all?

Thanks in advance!

[gpleiss]

The hyperparameters don’t have to be updated when you add new data. Updating them will give your model a better fit, but comes at the cost of additional computation.

GPyTorch doesn’t fit the hyperparameters of GP models automatically - you have to write the optimization loop yourself (as seen in our examples). You should:

• Condition your model on new data using set_train_data (and include the new data + all your old data)
• Re-run the training loop (if you want to update the hyperparameters)

However, you may find for your particular application that the hyperparameters don’t vary too much with new data - and it might make more sense to sparingly update the hyperparameters.

[Balandat]

That’s not easily possible with the same likelihood (the default one is homoskedastic). You could instead use a FixedNoiseGaussianLikelihood where you instantiate the first part of the vector of noises to the inferred noise level of your model and set the other ones to a very small noise level.