Data Distribution Valuation Using Generalized Bayesian Inference

Cuong N. Nguyen, Cuong V. Nguyen
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:2845-2853, 2026.

Abstract

We investigate the data distribution valuation problem, which aims to quantify the values of data distributions from their samples. This is a recently proposed problem that is related to but different from classical data valuation and can be applied to various applications. For this problem, we develop a novel framework called \emph{Generalized Bayes Valuation} that utilizes generalized Bayesian inference with a loss constructed from transferability measures. This framework allows us to solve, in a unified way, seemingly unrelated practical problems, such as annotator evaluation and data augmentation. Using the Bayesian principles, we further improve and enhance the applicability of our framework by extending it to the continuous data stream setting. Our experiment results confirm the effectiveness and efficiency of our framework in different real-world scenarios.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-nguyen26c, title = { Data Distribution Valuation Using Generalized Bayesian Inference }, author = {Nguyen, Cuong N. and Nguyen, Cuong V.}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {2845--2853}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/nguyen26c/nguyen26c.pdf}, url = {https://proceedings.mlr.press/v300/nguyen26c.html}, abstract = { We investigate the data distribution valuation problem, which aims to quantify the values of data distributions from their samples. This is a recently proposed problem that is related to but different from classical data valuation and can be applied to various applications. For this problem, we develop a novel framework called \emph{Generalized Bayes Valuation} that utilizes generalized Bayesian inference with a loss constructed from transferability measures. This framework allows us to solve, in a unified way, seemingly unrelated practical problems, such as annotator evaluation and data augmentation. Using the Bayesian principles, we further improve and enhance the applicability of our framework by extending it to the continuous data stream setting. Our experiment results confirm the effectiveness and efficiency of our framework in different real-world scenarios. } }
Endnote
%0 Conference Paper %T Data Distribution Valuation Using Generalized Bayesian Inference %A Cuong N. Nguyen %A Cuong V. Nguyen %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-nguyen26c %I PMLR %P 2845--2853 %U https://proceedings.mlr.press/v300/nguyen26c.html %V 300 %X We investigate the data distribution valuation problem, which aims to quantify the values of data distributions from their samples. This is a recently proposed problem that is related to but different from classical data valuation and can be applied to various applications. For this problem, we develop a novel framework called \emph{Generalized Bayes Valuation} that utilizes generalized Bayesian inference with a loss constructed from transferability measures. This framework allows us to solve, in a unified way, seemingly unrelated practical problems, such as annotator evaluation and data augmentation. Using the Bayesian principles, we further improve and enhance the applicability of our framework by extending it to the continuous data stream setting. Our experiment results confirm the effectiveness and efficiency of our framework in different real-world scenarios.
APA
Nguyen, C.N. & Nguyen, C.V.. (2026). Data Distribution Valuation Using Generalized Bayesian Inference . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:2845-2853 Available from https://proceedings.mlr.press/v300/nguyen26c.html.

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