Papers › Data Distribution Valuation

Data Distribution Valuation

6 Oct 2024arXiv:2410.04386archive 2025-07-28

Xinyi Xu, Shuaiqi Wang, Chuan-Sheng Foo, Bryan Kian Hsiang Low, Giulia Fanti

Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression).

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batched_rbf_mmd2 xinyiys/data_distribution_valuation/mmd.py official repository ran · our draft was wrong MIT (permissive) · 1682bb48a6f46300 · report
get_MMD_values_uneven xinyiys/data_distribution_valuation/mmd.py official repository ran · our draft was wrong MIT (permissive) · a0997c0acc0f3683 · report
get_extracted xinyiys/data_distribution_valuation/run_Ours.py official repository ran · honoured contract MIT (permissive) · f783a3b23476dfc6 · report
get_mean_se_df XinyiYS/Data_Distribution_Valuation/utils.py official repository ran MIT (permissive) · cbe4794453d07854 · report
get_trained_regressor XinyiYS/Data_Distribution_Valuation/regression/_Ours_utils.py official repository ran MIT (permissive) · 0e0b1ef1ccf79eb6 · report
huber XinyiYS/Data_Distribution_Valuation/data_utils.py official repository ran MIT (permissive) · 3f594ff441c3d230 · report
huber_regression XinyiYS/Data_Distribution_Valuation/regression/reg_data_utils.py official repository ran MIT (permissive) · fdbaf4694487b065 · report
linear_mmd2 XinyiYS/Data_Distribution_Valuation/mmd.py official repository ran fingerprinted MIT (permissive) · bb890357a4a87c70 · report
load_data XinyiYS/Data_Distribution_Valuation/utils.py official repository ran MIT (permissive) · 261fa51f4424898d · report
non_huber XinyiYS/Data_Distribution_Valuation/data_utils.py official repository ran MIT (permissive) · 3c8a7d889a9a2cbb · report
rbf_mmd2 xinyiys/data_distribution_valuation/mmd.py official repository ran · our draft was wrong MIT (permissive) · 48a3b455cac473ed · report
set_up_plotting XinyiYS/Data_Distribution_Valuation/utils.py official repository ran MIT (permissive) · 04a8c0a05ec0aaec · report
assign_data XinyiYS/Data_Distribution_Valuation/data_utils.py official repository unverified MIT (permissive) · 62cd4bed729e42af · report

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Data ValuationFraud DetectionIntrusion DetectionNetwork Intrusion Detection

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