# CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification

arXiv: https://arxiv.org/abs/2211.06800 · HTML: https://mlsanity.com/paper/2211.06800/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Estimating Training Data Influence by Tracing Gradient Descent](https://mlsanity.com/paper/2002.08484/) (2020)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2110.14049/) (2021)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Data Valuation using Reinforcement Learning](https://mlsanity.com/paper/1909.11671/) (2019)
- [A Distributional Framework for Data Valuation](https://mlsanity.com/paper/2002.12334/) (2020)
- [Collaborative Machine Learning with Incentive-Aware Model Rewards](https://mlsanity.com/paper/2010.12797/) (2020)
- [Efficient computation and analysis of distributional Shapley values](https://mlsanity.com/paper/2007.01357/) (2020)
- [Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset](https://mlsanity.com/paper/2010.08006/) (2020)
- [Data Shapley Valuation for Efficient Batch Active Learning](https://mlsanity.com/paper/2104.08312/) (2021)
- [Evaluating the Values of Sources in Transfer Learning](https://mlsanity.com/paper/2104.12567/) (2021)

## Built on by (most cited first, 30 of 32)

- [Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value](https://mlsanity.com/paper/2304.07718/) (2023)
- [Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values](https://mlsanity.com/paper/2306.10165/) (2023)
- [Redefining Contributions: Shapley-Driven Federated Learning](https://mlsanity.com/paper/2406.00569/) (2024)
- [DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation](https://mlsanity.com/paper/2306.02071/) (2023)
- [Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective](https://mlsanity.com/paper/2401.04374/) (2024)
- [Fairshare Data Pricing via Data Valuation for Large Language Models](https://mlsanity.com/paper/2502.00198/) (2025)
- [Towards Algorithmic Fairness by means of Instance-level Data Re-weighting based on Shapley Values](https://mlsanity.com/paper/2303.01928/) (2023)
- [Precedence-Constrained Winter Value for Effective Graph Data Valuation](https://mlsanity.com/paper/2402.01943/) (2024)
- [Data Distribution Valuation](https://mlsanity.com/paper/2410.04386/) (2024)
- [On the Inflation of KNN-Shapley Value](https://mlsanity.com/paper/2405.17489/) (2024)
- [Data-Centric AI in the Age of Large Language Models](https://mlsanity.com/paper/2406.14473/) (2024)
- [A Comprehensive Study of Shapley Value in Data Analytics](https://mlsanity.com/paper/2412.01460/) (2024)
- [Fast-DataShapley: Neural Modeling for Training Data Valuation](https://mlsanity.com/paper/2506.05281/) (2025)
- [Interpretable Triplet Importance for Personalized Ranking](https://mlsanity.com/paper/2407.19469/) (2024)
- [Towards Data Valuation via Asymmetric Data Shapley](https://mlsanity.com/paper/2411.00388/) (2024)
- [LossVal: Efficient Data Valuation for Neural Networks](https://mlsanity.com/paper/2412.04158/) (2024)
- [An Instrumental Value for Data Production and its Application to Data Pricing](https://mlsanity.com/paper/2412.18140/) (2024)
- [Unifying and Optimizing Data Values for Selection via Sequential Decision-Making](https://mlsanity.com/paper/2502.04554/) (2025)
- [Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value](https://mlsanity.com/paper/2505.16147/) (2025)
- [KAIROS: Scalable Model-Agnostic Data Valuation](https://mlsanity.com/paper/2506.23799/) (2025)
- [ShapE-GRPO: Shapley-Enhanced Reward Allocation for Multi-Candidate LLM Training](https://mlsanity.com/paper/2603.29871/) (2026)
- [Shapley Value on Probabilistic Classifiers](https://mlsanity.com/paper/2306.07171/) (2023)
- [Data Valuation by Fusing Global and Local Statistical Information](https://mlsanity.com/paper/2405.17464/) (2024)
- [Data Overvaluation Attack and Truthful Data Valuation in Federated Learning](https://mlsanity.com/paper/2502.00494/) (2025)
- [Dynamic Influence Tracker: Measuring Time-Varying Sample Influence During Training](https://mlsanity.com/paper/2502.10793/) (2025)
- [A case for data valuation transparency via DValCards](https://mlsanity.com/paper/2506.23349/) (2025)
- [f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness](https://mlsanity.com/paper/2510.10510/) (2025)
- [Hierarchical Dataset Selection for High-Quality Data Sharing](https://mlsanity.com/paper/2512.10952/) (2025)
- [GUDA: Counterfactual Group-wise Training Data Attribution for Diffusion Models via Unlearning](https://mlsanity.com/paper/2601.22651/) (2026)
- [SCARV: Structure-Constrained Aggregation for Stable Sample Ranking in Redundant NLP Datasets](https://mlsanity.com/paper/2605.00944/) (2026)

## Abstract (arXiv)

Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label detection. In particular, due to the desirable axiomatic properties, several Shapley value approximation methods have been proposed. In these methods, the value function is typically defined as the predictive accuracy over the entire development set. However, this limits the ability to differentiate between training instances that are helpful or harmful to their own classes. Intuitively, instances that harm their own classes may be noisy or mislabeled and should receive a lower valuation than helpful instances. In this work, we propose CS-Shapley, a Shapley value with a new value function that discriminates between training instances' in-class and out-of-class contributions. Our theoretical analysis shows the proposed value function is (essentially) the unique function that satisfies two desirable properties for evaluating data values in classification. Further, our experiments on two benchmark evaluation tasks (data removal and noisy label detection) and four classifiers demonstrate the effectiveness of CS-Shapley over existing methods. Lastly, we evaluate the "transferability" of data values estimated from one classifier to others, and our results suggest Shapley-based data valuation is transferable for application across different models.