A Distributional Framework For Data Valuation

Amirata Ghorbani, Michael Kim, James Zou
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:3535-3544, 2020.

Abstract

Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models. Despite its foundational role, a key limitation of the data Shapley framework is that it only provides valuations for points within a fixed data set. It does not account for statistical aspects of the data and does not give a way to reason about points outside the data set. To address these limitations, we propose a novel framework – distributional Shapley– where the value of a point is defined in the context of an underlying data distribution. We prove that distributional Shapley has several desirable statistical properties; for example, the values are stable under perturbations to the data points themselves and to the underlying data distribution. We leverage these properties to develop a new algorithm for estimating values from data, which comes with formal guarantees and runs two orders of magnitude faster than state-of-the-art algorithms for computing the (non distributional) data Shapley values. We apply distributional Shapley to diverse data sets and demonstrate its utility in a data market setting.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-ghorbani20a, title = {A Distributional Framework For Data Valuation}, author = {Ghorbani, Amirata and Kim, Michael and Zou, James}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {3535--3544}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/ghorbani20a/ghorbani20a.pdf}, url = {http://proceedings.mlr.press/v119/ghorbani20a.html}, abstract = {Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models. Despite its foundational role, a key limitation of the data Shapley framework is that it only provides valuations for points within a fixed data set. It does not account for statistical aspects of the data and does not give a way to reason about points outside the data set. To address these limitations, we propose a novel framework – distributional Shapley– where the value of a point is defined in the context of an underlying data distribution. We prove that distributional Shapley has several desirable statistical properties; for example, the values are stable under perturbations to the data points themselves and to the underlying data distribution. We leverage these properties to develop a new algorithm for estimating values from data, which comes with formal guarantees and runs two orders of magnitude faster than state-of-the-art algorithms for computing the (non distributional) data Shapley values. We apply distributional Shapley to diverse data sets and demonstrate its utility in a data market setting.} }
Endnote
%0 Conference Paper %T A Distributional Framework For Data Valuation %A Amirata Ghorbani %A Michael Kim %A James Zou %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-ghorbani20a %I PMLR %P 3535--3544 %U http://proceedings.mlr.press/v119/ghorbani20a.html %V 119 %X Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models. Despite its foundational role, a key limitation of the data Shapley framework is that it only provides valuations for points within a fixed data set. It does not account for statistical aspects of the data and does not give a way to reason about points outside the data set. To address these limitations, we propose a novel framework – distributional Shapley– where the value of a point is defined in the context of an underlying data distribution. We prove that distributional Shapley has several desirable statistical properties; for example, the values are stable under perturbations to the data points themselves and to the underlying data distribution. We leverage these properties to develop a new algorithm for estimating values from data, which comes with formal guarantees and runs two orders of magnitude faster than state-of-the-art algorithms for computing the (non distributional) data Shapley values. We apply distributional Shapley to diverse data sets and demonstrate its utility in a data market setting.
APA
Ghorbani, A., Kim, M. & Zou, J.. (2020). A Distributional Framework For Data Valuation. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:3535-3544 Available from http://proceedings.mlr.press/v119/ghorbani20a.html.

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