Update the default SingleTaskGP prior #2610
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 See title Differential Revision: D60080819
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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This pull request was exported from Phabricator. Differential Revision: D60080819 |
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Summary: Pull Request resolved: facebook#2610 X-link: pytorch/botorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Jul 29, 2024
Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
hvarfner
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: Pull Request resolved: facebook#2610 X-link: pytorch/botorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: Pull Request resolved: facebook#2610 X-link: pytorch/botorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
hvarfner
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Jul 30, 2024
Summary: X-link: facebook/Ax#2610 Pull Request resolved: pytorch#2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: saitcakmak Differential Revision: D60080819
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Summary: X-link: facebook/Ax#2610 Pull Request resolved: #2449 Update of the default hyperparameter priors for the SingleTaskGP. Switch from the conventional Scale-Matern kernel with Gamma(3, 6) lengthscale prior is substituted for an RBF Kernel (without a ScaleKernel), and a change from the high-noise Gamma(1.1, 0.05) noise prior of the GaussianLikelihood to a LogNormal prior that prefers lower values. The change is made in accordance with the findings of [1]. The change is made to improve the out-of-the-box performance of the BoTorch models on high-dimensional problems. [1] Carl Hvarfner, Erik Orm Hellsten, Luigi Nardi. _Vanilla Bayesian Optimization Performs Great in High Dimensions_. ICML, 2024. Reviewed By: dme65, saitcakmak Differential Revision: D60080819 fbshipit-source-id: d55ff91dee9949cbd7f5828531644fc001cb3e22
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Summary:
X-link: pytorch/botorch#2449
See title
Differential Revision: D60080819