MCGrad: Multicalibration at Web Scale
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915747703291904 |
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| author | Tax, Niek Perini, Lorenzo Linder, Fridolin Haimovich, Daniel Karamshuk, Dima Okati, Nastaran Vojnovic, Milan Apostolopoulos, Pavlos Athanasios |
| author_facet | Tax, Niek Perini, Lorenzo Linder, Fridolin Haimovich, Daniel Karamshuk, Dima Okati, Nastaran Vojnovic, Milan Apostolopoulos, Pavlos Athanasios |
| contents | We propose MCGrad, a novel and scalable multicalibration algorithm. Multicalibration - calibration in subgroups of the data - is an important property for the performance of machine learning-based systems. Existing multicalibration methods have thus far received limited traction in industry. We argue that this is because existing methods (1) require such subgroups to be manually specified, which ML practitioners often struggle with, (2) are not scalable, or (3) may harm other notions of model performance such as log loss and Area Under the Precision-Recall Curve (PRAUC). MCGrad does not require explicit specification of protected groups, is scalable, and often improves other ML evaluation metrics instead of harming them. MCGrad has been in production at Meta, and is now part of hundreds of production models. We present results from these deployments as well as results on public datasets. We provide an open source implementation of MCGrad at https://github.com/facebookincubator/MCGrad. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MCGrad: Multicalibration at Web Scale Tax, Niek Perini, Lorenzo Linder, Fridolin Haimovich, Daniel Karamshuk, Dima Okati, Nastaran Vojnovic, Milan Apostolopoulos, Pavlos Athanasios Machine Learning We propose MCGrad, a novel and scalable multicalibration algorithm. Multicalibration - calibration in subgroups of the data - is an important property for the performance of machine learning-based systems. Existing multicalibration methods have thus far received limited traction in industry. We argue that this is because existing methods (1) require such subgroups to be manually specified, which ML practitioners often struggle with, (2) are not scalable, or (3) may harm other notions of model performance such as log loss and Area Under the Precision-Recall Curve (PRAUC). MCGrad does not require explicit specification of protected groups, is scalable, and often improves other ML evaluation metrics instead of harming them. MCGrad has been in production at Meta, and is now part of hundreds of production models. We present results from these deployments as well as results on public datasets. We provide an open source implementation of MCGrad at https://github.com/facebookincubator/MCGrad. |
| title | MCGrad: Multicalibration at Web Scale |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.19884 |