RobustRelevancePursuitSingleTaskGP with specialized fit_gpytorch_mll
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Codecov ReportAll modified and coverable lines are covered by tests ✅
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## main #2690 +/- ##
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…ll` (pytorch#2690) Summary: This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Differential Revision: D68353582
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…ll` (pytorch#2690) Summary: This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Differential Revision: D68353582
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…ll` (pytorch#2690) Summary: This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Reviewed By: esantorella Differential Revision: D68353582
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…ll` (pytorch#2690) Summary: This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Reviewed By: esantorella Differential Revision: D68353582
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…ll` (pytorch#2690) Summary: This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Reviewed By: esantorella Differential Revision: D68353582
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…ll` (pytorch#2690) Summary: This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Reviewed By: esantorella Differential Revision: D68353582
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Summary: Pull Request resolved: pytorch#2694 This commit open-sources the MAP-SAAS model, originally implemented by dme65 to provide a more efficient alternative to the fully Bayesian SAAS model, and makes changes to ensure that GPU-based computation is supported. Differential Revision: D68522782 Reviewed By: Balandat
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…ll` (pytorch#2690) Summary: Pull Request resolved: pytorch#2690 This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Reviewed By: esantorella Differential Revision: D68353582
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This pull request was exported from Phabricator. Differential Revision: D68353582 |
…ll` (pytorch#2690) Summary: Pull Request resolved: pytorch#2690 This commit introduces an abstract `RobustRelevancePursuitModel` and `RobustRelevancePursuitSingleTaskGP`, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonical `SingleTaskGP`, but automatically extend the likelihood with the `SparseOutlierGaussianLikelihood`, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization via `fit_gpytorch_mll` by dispatching on the model type. This makes the model and algorithm easy to use. Reviewed By: esantorella Differential Revision: D68353582
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This pull request has been merged in 18b19e2. |
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Summary: This commit introduces an abstract
RobustRelevancePursuitModelandRobustRelevancePursuitSingleTaskGP, a specific implementation of the abstract class. The main purpose of the new class is to provide an identical interface to a canonicalSingleTaskGP, but automatically extend the likelihood with theSparseOutlierGaussianLikelihood, and toggle the Relevance Pursuit algorithm automatically through the marginal likelihood optimization viafit_gpytorch_mllby dispatching on the model type. This makes the model and algorithm easy to use.Differential Revision: D68353582