Decoupled Acquisition Function #1948
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This pull request was exported from Phabricator. Differential Revision: D47710904 |
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Summary: Pull Request resolved: pytorch#1948 Introduce an abstract class for decoupled acquisition functions Reviewed By: esantorella Differential Revision: D47710904 fbshipit-source-id: 75a5c795ddcc9058bdc75db27bbc3a6a0ad71ade
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Summary: Pull Request resolved: pytorch#1948 Introduce an abstract class for decoupled acquisition functions. A decoupled acquisition function where one may intend to evaluate a design on only a subset of the outcomes. Typically this would be handled by fantasizing, where one would fantasize as to what the partial observation would be if one were to evaluate a design on the subset of outcomes (e.g. you only fantasize at those outcomes) Reviewed By: esantorella Differential Revision: D47710904 fbshipit-source-id: 2c4cd9c0818467c2fd13c4b02c9a2f68734dc3fb
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Summary: Pull Request resolved: pytorch#1948 Introduce an abstract class for decoupled acquisition functions. A decoupled acquisition function where one may intend to evaluate a design on only a subset of the outcomes. Typically this would be handled by fantasizing, where one would fantasize as to what the partial observation would be if one were to evaluate a design on the subset of outcomes (e.g. you only fantasize at those outcomes) Differential Revision: https://internalfb.com/D47710904 fbshipit-source-id: b10608b5ab8feaebcb209cab0e6e95f029cf0fbe
Codecov Report
@@ Coverage Diff @@
## main #1948 +/- ##
=======================================
Coverage 99.94% 99.94%
=======================================
Files 177 178 +1
Lines 15693 15746 +53
=======================================
+ Hits 15685 15738 +53
Misses 8 8
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sdaulton
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Summary: Pull Request resolved: pytorch#1948 Introduce an abstract class for decoupled acquisition functions. A decoupled acquisition function where one may intend to evaluate a design on only a subset of the outcomes. Typically this would be handled by fantasizing, where one would fantasize as to what the partial observation would be if one were to evaluate a design on the subset of outcomes (e.g. you only fantasize at those outcomes) Differential Revision: https://internalfb.com/D47710904 fbshipit-source-id: e31fb21afdca0f65ab96e5d1d124b0eaa815ecb6
Summary: Pull Request resolved: pytorch#1948 Introduce an abstract class for decoupled acquisition functions. A decoupled acquisition function where one may intend to evaluate a design on only a subset of the outcomes. Typically this would be handled by fantasizing, where one would fantasize as to what the partial observation would be if one were to evaluate a design on the subset of outcomes (e.g. you only fantasize at those outcomes) Reviewed By: esantorella Differential Revision: D47710904 fbshipit-source-id: e61b3555c5fd93b53990ce3af299650bbb5341e1
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Summary: Pull Request resolved: pytorch#1948 Introduce an abstract class for decoupled acquisition functions. A decoupled acquisition function where one may intend to evaluate a design on only a subset of the outcomes. Typically this would be handled by fantasizing, where one would fantasize as to what the partial observation would be if one were to evaluate a design on the subset of outcomes (e.g. you only fantasize at those outcomes) Differential Revision: https://internalfb.com/D47710904 fbshipit-source-id: 1dbebeaa4fac09a74f25cfc441bef4dcf5fb7d3f
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Summary: Pull Request resolved: pytorch#1948 Introduce an abstract class for decoupled acquisition functions. A decoupled acquisition function where one may intend to evaluate a design on only a subset of the outcomes. Typically this would be handled by fantasizing, where one would fantasize as to what the partial observation would be if one were to evaluate a design on the subset of outcomes (e.g. you only fantasize at those outcomes) Differential Revision: https://internalfb.com/D47710904 fbshipit-source-id: 6fdcc76ada89028cc48c0df5644a93a7c65e73d8
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Summary: Introduce an abstract class for decoupled acquisition functions
Differential Revision: D47710904