Use StratifiedStandardize for per-task Y standardization in TL (#5194)#5194
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Use StratifiedStandardize for per-task Y standardization in TL (#5194)#5194hvarfner wants to merge 3 commits into
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…TaskGP (facebook#5192) Summary: X-link: meta-pytorch/botorch#3296 Automatically configures learned feature imputation for models that pad heterogeneous per-task data to the full joint feature space. Models with native heterogeneity support are excluded from this automatic configuration. Differential Revision: D101841497
facebook#5193) Summary: Switches the default heterogeneous transfer learning model from a specialized per-task kernel model to a standard multi-task GP with learned feature imputation. The previous default model class is marked as deprecated. Differential Revision: D102197137
…ook#5194) Summary: Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms. This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments. Differential Revision: D102197139
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…ook#5194) Summary: Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms. This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments. Differential Revision: D102197139
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Coverage 96.38% 96.38%
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Files 617 617
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Summary:
Adds per-task outcome standardization to the transfer learning adapter, ensuring each task's observations are standardized independently rather than jointly. Updates the default transform pipeline to use TL-specific outcome transforms.
This removes ambiguity on whether the right transforms have been applied (e.g. QuickBO/warm-starting), where standardization is not performed across, but within experiments.
Differential Revision: D102197139