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Robust Gaussian Processes via Relevance Pursuit #2608

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SebastianAment
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@SebastianAment SebastianAment commented Nov 1, 2024 •
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Summary: This commit adds the implementation of the Robust Gaussian Processes via Relevance Pursuit models and algorithms of the NeurIPS 2024 article.

Differential Revision: D65343571

@facebook-github-bot facebook-github-bot added the CLA Signed Do not delete this pull request or issue due to inactivity. label Nov 1, 2024
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This pull request was exported from Phabricator. Differential Revision: D65343571

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codecov bot commented Nov 1, 2024 •
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Codecov Report

Attention: Patch coverage is 86.11670% with 69 lines in your changes missing coverage. Please review.

Project coverage is 99.60%. Comparing base (3ca48d0) to head (e6386c6).

Files with missing lines Patch % Lines
botorch/models/relevance_pursuit.py 78.38% 67 Missing ⚠️
botorch/models/likelihoods/sparse_outlier_noise.py 98.43% 2 Missing ⚠️
Additional details and impacted files 
@@            Coverage Diff             @@
##             main    #2608      +/-   ##
==========================================
- Coverage   99.98%   99.60%   -0.39%     
==========================================
  Files         196      198       +2     
  Lines       17372    17866     +494     
==========================================
+ Hits        17370    17795     +425     
- Misses          2       71      +69

☔ View full report in Codecov by Sentry.
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SebastianAment added a commit to SebastianAment/botorch that referenced this pull request Nov 4, 2024
@SebastianAment @facebook-github-bot
Robust Gaussian Processes via Relevance Pursuit (pytorch#2608)
fad64bf 
Summary:
Pull Request resolved: pytorch#2608

This commit adds the implementation of the [Robust Gaussian Processes via Relevance Pursuit](https://arxiv.org/pdf/2410.24222) models and algorithms of the NeurIPS 2024 article.

Differential Revision: D65343571
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This pull request was exported from Phabricator. Differential Revision: D65343571

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This pull request was exported from Phabricator. Differential Revision: D65343571

SebastianAment added a commit to SebastianAment/botorch that referenced this pull request Nov 5, 2024
@SebastianAment @facebook-github-bot
Robust Gaussian Processes via Relevance Pursuit (pytorch#2608)
5ce9666 
Summary:
Pull Request resolved: pytorch#2608

This commit adds the implementation of the [Robust Gaussian Processes via Relevance Pursuit](https://arxiv.org/pdf/2410.24222) models and algorithms of the NeurIPS 2024 article.

Differential Revision: D65343571
@facebook-github-bot
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This pull request was exported from Phabricator. Differential Revision: D65343571

SebastianAment added a commit to SebastianAment/botorch that referenced this pull request Nov 5, 2024
@SebastianAment @facebook-github-bot
Robust Gaussian Processes via Relevance Pursuit (pytorch#2608)
9f3622c 
Summary:
Pull Request resolved: pytorch#2608

This commit adds the implementation of the [Robust Gaussian Processes via Relevance Pursuit](https://arxiv.org/pdf/2410.24222) models and algorithms of the NeurIPS 2024 article.

Differential Revision: D65343571
@SebastianAment @facebook-github-bot
Robust Gaussian Processes via Relevance Pursuit (pytorch#2608)
e6386c6 
Summary:
Pull Request resolved: pytorch#2608

This commit adds the implementation of the [Robust Gaussian Processes via Relevance Pursuit](https://arxiv.org/pdf/2410.24222) models and algorithms of the NeurIPS 2024 article.

Differential Revision: D65343571
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This pull request was exported from Phabricator. Differential Revision: D65343571

@jduerholt
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I just read the paper, and really like it! We will definitely integrate it into our workflows, as this problem is very common for our experimental data. Currently we are sometimes using iterative trimming.

Any plan when this will land in main?

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Great to hear @jduerholt! I'll just need to get test coverage to 100%, will try to get the time for this within the next two weeks. Would be curious to learn about your experience if / when you start using the model.

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jduerholt commented Nov 8, 2024 •
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I will update you, as soon as it will land here in main, I will integrate it into our workflows ;) And then update you on our experience.

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