# Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning

arXiv: https://arxiv.org/abs/2107.06336 · HTML: https://mlsanity.com/paper/2107.06336/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [A PAC Approach to Application-Specific Algorithm Selection](https://mlsanity.com/paper/1511.07147/) (2015)
- [Statistical Cost Sharing](https://mlsanity.com/paper/1703.03111/) (2017)
- [Learning Cooperative Games](https://mlsanity.com/paper/1505.00039/) (2015)
- [Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions](https://mlsanity.com/paper/2006.09538/) (2020)

## Built on by (most cited first, 13 of 13)

- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution](https://mlsanity.com/paper/2401.15866/) (2024)
- [Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation](https://mlsanity.com/paper/2308.15709/) (2023)
- [An Economic Solution to Copyright Challenges of Generative AI](https://mlsanity.com/paper/2404.13964/) (2024)
- [A Note on "Towards Efficient Data Valuation Based on the Shapley Value''](https://mlsanity.com/paper/2302.11431/) (2023)
- [Influence Guided Context Selection for Effective Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.21359/) (2025)
- [The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research](https://mlsanity.com/paper/2310.07882/) (2023)
- [On the Inflation of KNN-Shapley Value](https://mlsanity.com/paper/2405.17489/) (2024)
- [DUPRE: Data Utility Prediction for Efficient Data Valuation](https://mlsanity.com/paper/2502.16152/) (2025)
- [Thresholding Data Shapley for Data Cleansing Using Multi-Armed Bandits](https://mlsanity.com/paper/2402.08209/) (2024)
- [Unifying and Optimizing Data Values for Selection via Sequential Decision-Making](https://mlsanity.com/paper/2502.04554/) (2025)
- [Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation](https://mlsanity.com/paper/2503.18195/) (2025)
- [Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation](https://mlsanity.com/paper/2607.03675/) (2026)

## Abstract (arXiv)

The Shapley value (SV) and Least core (LC) are classic methods in cooperative game theory for cost/profit sharing problems. Both methods have recently been proposed as a principled solution for data valuation tasks, i.e., quantifying the contribution of individual datum in machine learning. However, both SV and LC suffer computational challenges due to the need for retraining models on combinatorially many data subsets. In this work, we propose to boost the efficiency in computing Shapley value or Least core by learning to estimate the performance of a learning algorithm on unseen data combinations. Theoretically, we derive bounds relating the error in the predicted learning performance to the approximation error in SV and LC. Empirically, we show that the proposed method can significantly improve the accuracy of SV and LC estimation.