qLogNEI #1937
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qLogNEI #1937
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@@ Coverage Diff @@
## main #1937 +/- ##
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Coverage 99.94% 99.94%
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Files 177 177
Lines 15596 15655 +59
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+ Hits 15588 15647 +59
Misses 8 8
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Summary: Pull Request resolved: pytorch#1937 This commit introduces `qLogNoisyExpectedImprovement` (`qLogNEI`) a cousing of `qLogEI`. Similar to `qLogEI` and in contrast to `q(N)EI`, it generally exhibits strong and smooth gradients, leading to better acquisition function optimization and Bayesian optimization as a result. Differential Revision: D47439161 fbshipit-source-id: fbe7fd2c86131a30557cb55ceeb35f8524e691a3
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Summary: Pull Request resolved: pytorch#1937 This commit introduces `qLogNoisyExpectedImprovement` (`qLogNEI`) a cousing of `qLogEI`. Similar to `qLogEI` and in contrast to `q(N)EI`, it generally exhibits strong and smooth gradients, leading to better acquisition function optimization and Bayesian optimization as a result. Differential Revision: D47439161 fbshipit-source-id: 9553b0123f5e6d9244982552f18da9ff4c8b3371
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Summary: Pull Request resolved: pytorch#1937 This commit introduces `qLogNoisyExpectedImprovement` (`qLogNEI`) a cousing of `qLogEI`. Similar to `qLogEI` and in contrast to `q(N)EI`, it generally exhibits strong and smooth gradients, leading to better acquisition function optimization and Bayesian optimization as a result. Differential Revision: D47439161 fbshipit-source-id: f16d9090c37a7a3f9f49edd306ed8d6fb7fbf706
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Summary: Pull Request resolved: pytorch#1937 This commit introduces `qLogNoisyExpectedImprovement` (`qLogNEI`) a cousin of `qLogEI`. Similar to `qLogEI` and in contrast to `q(N)EI`, it generally exhibits strong and smooth gradients, leading to better acquisition function optimization and Bayesian optimization as a result. Reviewed By: Balandat Differential Revision: D47439161 fbshipit-source-id: c6556e3f48022a972f257de6f1d0f153bec0f5e6
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Summary: Pull Request resolved: pytorch#1937 This commit introduces `qLogNoisyExpectedImprovement` (`qLogNEI`) a cousin of `qLogEI`. Similar to `qLogEI` and in contrast to `q(N)EI`, it generally exhibits strong and smooth gradients, leading to better acquisition function optimization and Bayesian optimization as a result. Differential Revision: https://internalfb.com/D47439161 fbshipit-source-id: 8a50f123e2f4a828934c8bc4fba0a163e0c0abb4
Summary: Pull Request resolved: pytorch#1937 This commit introduces `qLogNoisyExpectedImprovement` (`qLogNEI`) a cousin of `qLogEI`. Similar to `qLogEI` and in contrast to `q(N)EI`, it generally exhibits strong and smooth gradients, leading to better acquisition function optimization and Bayesian optimization as a result. Reviewed By: Balandat Differential Revision: D47439161 fbshipit-source-id: 8fd1aec52761dfc16b22ddd39f099cd207625966
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Summary: This commit introduces
qLogNoisyExpectedImprovement(qLogNEI) a cousing ofqLogEI. Similar toqLogEIand in contrast toq(N)EI, it generally exhibits strong and smooth gradients, leading to better acquisition function optimization and Bayesian optimization as a result.Differential Revision: D47439161