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* Cleans up the `GaussianProcessSurrogate` class * Adds transfer learning tests asserting that the mechanism works regardless of which tasks are represented in the training data
* Define mean functions for the available presets * Pass both mean and likelihood when instantiating preset
Merged
Two of the three cases effectively execute the same code and can thus be merged
Sphinx seems to need them
This was referenced Mar 6, 2026
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DevPR, parent is #745 * Generalizes the existing GP kernel into multi-purpose component logic using generics * Adds support for GPyTorch GP components * Enables configuration of GP mean and likelihood * Adds `EDBO` and `SMOOTHED_EDBO` GP preset * Completes preset logic by including mean and likelihood components
DevPR, parent is #745 Adds the `IndexKernel` and `PositiveIndexKernel` classes The new factory design added in #746 offers full customization of the GP, following a composition approach. To reach the full potential that this offers, having BayBE `IndexKernel` classes to let the user to construct custom architectures is the natural next step, in particular because there other reasonable choices than just having the standard ICM product, such as: * Selecting the type of index kernel (e.g. regular vs positive) * Implementing multi-fidelity approaches (see #715) * Creating kernels that can handle multiple task parameters In addition: * It finally provides an interface for configuring the kernel itself, e.g. its rank (which used to be a TODO for ages) * And it offers the new parameter-named BayBE for conveniently specifying active dimensions for the above tasks.
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Rough agenda:
#743 (Cleanup)
#746 (GP Components)
EDBO/SMOOTHED_EDBOpresetkernelmachinery intocomponentmachinery using genericsgpytorchGP components#747 (Index Kernel)
IndexKernelclassPositiveIndexKernelclass#748 (Active Dimensions)
IndexKernellogic into kernel factory#757 (BoTorch Preset)
BOTORCHpresetOpen TODO
gpytorchGP componentsBOTORCHpreset as defaultCHENpresetSettingattribute