acquisition function wrapper #1532
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: D41629186 fbshipit-source-id: 6dcfdb4ebf4dd316f361d1d3b0c9bfa1d54ff2d1
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: D41629186 fbshipit-source-id: c52722b2946207e219ad5f49e6fa314706cdd953
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: https://internalfb.com/D41629186 fbshipit-source-id: 8696eab8f95898528188a7d5520a9c48165ac914
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: https://internalfb.com/D41629186 fbshipit-source-id: baa8ead5996c28185a770e7c4d195f08a3b96968
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: https://internalfb.com/D41629186 fbshipit-source-id: d39c44320682133dd09956d8eea9d41196652198
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This pull request was exported from Phabricator. Differential Revision: D41629186 |
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: D41629186 fbshipit-source-id: 7d77fee09746b10e6533372621d9c49df8d8ab5a
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Codecov Report
@@ Coverage Diff @@
## main #1532 +/- ##
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Coverage 100.00% 100.00%
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Files 169 170 +1
Lines 14518 14525 +7
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+ Hits 14518 14525 +7
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: D41629186 fbshipit-source-id: f27ddc07ba0dd1dc5eb5e91f8f1d00ebd55df49f
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Feb 15, 2023
Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: https://internalfb.com/D41629186 fbshipit-source-id: 181450760c829d5e6b9309f7ee1d07428699b02a
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: https://internalfb.com/D41629186 fbshipit-source-id: c2d3b339edf44a3167804b095d213b3ba98b5e13
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Summary: Pull Request resolved: pytorch#1532 Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs Differential Revision: https://internalfb.com/D41629186 fbshipit-source-id: 51b84765e58c17cda63bc582bfe30d0ca13955b5
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Summary: Add a wrapper for modifying inputs/outputs. This is useful for not only probabilistic reparameterization, but will also simplify other integrated AFs (e.g. MCMC) as well as fixed feature AFs and things like prior-guided AFs
Differential Revision: D41629186