Efficient leave-one-out cross-validation for ensemble models #3103
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…torch#3103) Summary: This commit introduces a new function `ensemble_loo_cv` in `botorch/cross_validation.py` for performing efficient leave-one-out cross-validation (LOOCV) on ensemble models, in addition to `loo_cv`, which automatically dispatches to the correct efficient CV implementation (ensemble, or non-ensemble), dependent on the `_is_ensemble` attribute. Differential Revision: D88506875
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…torch#3103) Summary: This commit introduces a new function `ensemble_loo_cv` in `botorch/cross_validation.py` for performing efficient leave-one-out cross-validation (LOOCV) on ensemble models, in addition to `loo_cv`, which automatically dispatches to the correct efficient CV implementation (ensemble, or non-ensemble), dependent on the `_is_ensemble` attribute. Differential Revision: D88506875
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…-pytorch#3098) Summary: BoTorch currently supports a naive version of leave-one-out (LOO) cross-validation, which entails generating all folds directly and evaluating the model on all folds separately. However, for Gaussian processes, there is an efficient way to compute all LOO results (LOO predictive means and variances) with a single linear-algebraic formula with a O(n^3) complexity, whereas the naive approach takes O(n^3) per fold, so O(n^4) in total. This commit implements the effient LOO CV method for non-ensemble BoTorch models whose prior marginal distributions are a multi-variate Normals. Reviewed By: Balandat Differential Revision: D88273413
…torch#3103) Summary: This commit introduces a new function `ensemble_loo_cv` in `botorch/cross_validation.py` for performing efficient leave-one-out cross-validation (LOOCV) on ensemble models, in addition to `loo_cv`, which automatically dispatches to the correct efficient CV implementation (ensemble, or non-ensemble), dependent on the `_is_ensemble` attribute. Reviewed By: Balandat Differential Revision: D88506875
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Summary: This commit introduces a new function
ensemble_loo_cvinbotorch/cross_validation.pyfor performing efficient leave-one-out cross-validation (LOOCV) on ensemble models, in addition toloo_cv, which automatically dispatches to the correct efficient CV implementation (ensemble, or non-ensemble), dependent on the_is_ensembleattribute.Differential Revision: D88506875