{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/efficient-task-specific-data-valuation-for","title":"Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms","arxiv_id":"1908.08619","date":"2019-08-22","proceeding":null,"authors":["Ruoxi Jia","David Dao","Boxin Wang","Frances Ann Hubis","Nezihe Merve Gurel","Bo Li","Ce Zhang","Costas J. Spanos","Dawn Song"],"abstract":"Given a data set $\\mathcal{D}$ containing millions of data points and a data consumer who is willing to pay for \\$$X$ to train a machine learning (ML) model over $\\mathcal{D}$, how should we distribute this \\$$X$ to each data point to reflect its \"value\"? In this paper, we define the \"relative value of data\" via the Shapley value, as it uniquely possesses properties with appealing real-world interpretations, such as fairness, rationality and decentralizability. For general, bounded utility functions, the Shapley value is known to be challenging to compute: to get Shapley values for all $N$ data points, it requires $O(2^N)$ model evaluations for exact computation and $O(N\\log N)$ for $(\\epsilon, \\delta)$-approximation. In this paper, we focus on one popular family of ML models relying on $K$-nearest neighbors ($K$NN). The most surprising result is that for unweighted $K$NN classifiers and regressors, the Shapley value of all $N$ data points can be computed, exactly, in $O(N\\log N)$ time -- an exponential improvement on computational complexity! Moreover, for $(\\epsilon, \\delta)$-approximation, we are able to develop an algorithm based on Locality Sensitive Hashing (LSH) with only sublinear complexity $O(N^{h(\\epsilon,K)}\\log N)$ when $\\epsilon$ is not too small and $K$ is not too large. We empirically evaluate our algorithms on up to $10$ million data points and even our exact algorithm is up to three orders of magnitude faster than the baseline approximation algorithm. The LSH-based approximation algorithm can accelerate the value calculation process even further. We then extend our algorithms to other scenarios such as (1) weighed $K$NN classifiers, (2) different data points are clustered by different data curators, and (3) there are data analysts providing computation who also requires proper valuation.","url_abs":"https://arxiv.org/abs/1908.08619v4","url_pdf":"https://arxiv.org/pdf/1908.08619v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"efficient-task-specific-data-valuation-for","repo_url":"https://github.com/AI-secure/KNN-PVLDB","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"efficient-task-specific-data-valuation-for","repo_url":"https://github.com/jiachen-t-wang/softlabel-knnsv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"efficient-task-specific-data-valuation-for","repo_url":"https://github.com/aai-institute/pyDVL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"LGPL-3.0"}}],"tasks":[{"task_slug":"data-valuation","task_name":"Data Valuation"},{"task_slug":"fairness","task_name":"Fairness"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1908.08619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.08619"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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