Tutorial for Robust Gaussian Processes via Relevance Pursuit #2707
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…#2707) Summary: This commit adds a tutorial of the robust Relevance Pursuit model, specifically using the `RobustRelevancePursuitSingleTaskGP` and the Twitter flash crash examples, as well as a synthetic regression example that showcases both usage patterns, as well as ways of analyzing the results of the model. Test Plan: Ran the tutorial on an internal devserver, my local machine, and the smoke test passes in the [OSS test](https://github.com/pytorch/botorch/actions/runs/13021130751/job/36321841195?pr=2707): ``` Running tutorial relevance_pursuit_robust_regression.ipynb. Running tutorial relevance_pursuit_robust_regression.ipynb took 7.77 seconds. Memory usage started at 47.5 MB and the maximum was 798.1875 MB. ``` ## Twitter Flash Crash Example {F1974794186}{F1974794179} Reviewed By: Balandat Differential Revision: D68353581 Pulled By: SebastianAment
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…#2707) Summary: This commit adds a tutorial of the robust Relevance Pursuit model, specifically using the `RobustRelevancePursuitSingleTaskGP` and the Twitter flash crash examples, as well as a synthetic regression example that showcases both usage patterns, as well as ways of analyzing the results of the model. Test Plan: Ran the tutorial on an internal devserver, my local machine, and the smoke test passes in the [OSS test](https://github.com/pytorch/botorch/actions/runs/13021130751/job/36321841195?pr=2707): ``` Running tutorial relevance_pursuit_robust_regression.ipynb. Running tutorial relevance_pursuit_robust_regression.ipynb took 7.77 seconds. Memory usage started at 47.5 MB and the maximum was 798.1875 MB. ``` ## Twitter Flash Crash Example {F1974794186}{F1974794179} Reviewed By: Balandat Differential Revision: D68353581 Pulled By: SebastianAment
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…#2707) Summary: This commit adds a tutorial of the robust Relevance Pursuit model, specifically using the `RobustRelevancePursuitSingleTaskGP` and the Twitter flash crash examples, as well as a synthetic regression example that showcases both usage patterns, as well as ways of analyzing the results of the model. Test Plan: Ran the tutorial on an internal devserver, my local machine, and the smoke test passes in the [OSS test](https://github.com/pytorch/botorch/actions/runs/13021130751/job/36321841195?pr=2707): ``` Running tutorial relevance_pursuit_robust_regression.ipynb. Running tutorial relevance_pursuit_robust_regression.ipynb took 7.77 seconds. Memory usage started at 47.5 MB and the maximum was 798.1875 MB. ``` ## Twitter Flash Crash Example {F1974794186}{F1974794179} Reviewed By: Balandat Differential Revision: D68353581 Pulled By: SebastianAment
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…#2707) Summary: This commit adds a tutorial of the robust Relevance Pursuit model, specifically using the `RobustRelevancePursuitSingleTaskGP` and the Twitter flash crash examples, as well as a synthetic regression example that showcases both usage patterns, as well as ways of analyzing the results of the model. ## Twitter Flash Crash <img width="1007" alt="Screenshot 2025-01-29 at 1 18 10 PM" src="https://github.com/user-attachments/assets/da292e69-eb67-4f49-b6c1-cfc1583a057a" /> <img width="1009" alt="Screenshot 2025-01-29 at 1 18 24 PM" src="https://github.com/user-attachments/assets/da3b578d-18e6-4889-ba4c-34828a152cc1" /> ## Synthetic Example <img width="990" alt="Screenshot 2025-01-29 at 1 19 01 PM" src="https://github.com/user-attachments/assets/cacb09ca-d2e3-448d-9715-bfc70fc6a41a" /> <img width="1010" alt="Screenshot 2025-01-29 at 1 19 10 PM" src="https://github.com/user-attachments/assets/020596cb-096d-4923-b405-93433f95bbf2" /> <img width="1000" alt="Screenshot 2025-01-29 at 1 19 22 PM" src="https://github.com/user-attachments/assets/0f968610-c2b2-421f-9dc6-16aebf68024d" /> Reviewed By: Balandat Differential Revision: D68353581 Pulled By: SebastianAment
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…#2707) Summary: This commit adds a tutorial of the robust Relevance Pursuit model, specifically using the `RobustRelevancePursuitSingleTaskGP` and the Twitter flash crash examples, as well as a synthetic regression example that showcases both usage patterns, as well as ways of analyzing the results of the model. ## Twitter Flash Crash <img width="1007" alt="Screenshot 2025-01-29 at 1 18 10 PM" src="https://github.com/user-attachments/assets/da292e69-eb67-4f49-b6c1-cfc1583a057a" /> <img width="1009" alt="Screenshot 2025-01-29 at 1 18 24 PM" src="https://github.com/user-attachments/assets/da3b578d-18e6-4889-ba4c-34828a152cc1" /> ## Synthetic Example <img width="990" alt="Screenshot 2025-01-29 at 1 19 01 PM" src="https://github.com/user-attachments/assets/cacb09ca-d2e3-448d-9715-bfc70fc6a41a" /> <img width="1010" alt="Screenshot 2025-01-29 at 1 19 10 PM" src="https://github.com/user-attachments/assets/020596cb-096d-4923-b405-93433f95bbf2" /> <img width="1000" alt="Screenshot 2025-01-29 at 1 19 22 PM" src="https://github.com/user-attachments/assets/0f968610-c2b2-421f-9dc6-16aebf68024d" /> Reviewed By: Balandat Differential Revision: D68353581 Pulled By: SebastianAment
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@SebastianAment merged this pull request in 472de87. |
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@jduerholt in case you are still interested in trying the robust model out for your use case: this tutorial shows how to use the robust model in a very similar way to a canonical |
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@SebastianAment: I assume you mean this one: #2707 (comment) Honestly, this is exactly what I was waiting for, as I was looking to the raw implementation and it was not fully clear to me how to actually use it (without investing more time into it). Based on this tutorial, it is super easy to implement it into our workflows. Thank you very much! |
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Summary: This commit adds a tutorial of the robust Relevance Pursuit model, specifically using the
RobustRelevancePursuitSingleTaskGPand the Twitter flash crash examples, as well as a synthetic regression example that showcases both usage patterns, as well as ways of analyzing the results of the model.Twitter Flash Crash
Synthetic Example
Differential Revision: D68353581