| Towards Efficient Data Valuation Based on the Shapley Value |
Ruoxi Jia & David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, Costas J. Spanos |
2019 |
Summary Jia et al. (2019) contribute theoretical and practical results for efficient methods for approximating the Shapley value (SV). They show that methods with a sublinear amount of model evaluations are possible and further reductions can be made for sparse SVs. Lastly, they introduce two practical SV estimation methods for ML tasks, one for uniformly stable learning algorithms and one for smooth loss functions. |
Bibtex@inproceedings{jia2019towards,
title={Towards efficient data valuation based on the shapley value},
author={Jia, Ruoxi and Dao, David and Wang, Boxin and Hubis, Frances Ann and Hynes, Nick and G{"u}rel, Nezihe Merve and Li, Bo and Zhang, Ce and Song, Dawn and Spanos, Costas J},
booktitle={The 22nd International Conference on Artificial Intelligence and Statistics},
pages={1167--1176},
year={2019},
organization={PMLR}
} |
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| Data Shapley: Equitable Valuation of Data for Machine Learning |
Amirata Ghorbani, James Zou |
2019 |
Summary Ghorbani & Zou (2019) introduce (data) Shapley value to equitably measure the value of each training point to a supervised learners performance. They further outline several benefits of the Shapley value, e.g. being able to capture outliers or inform what new data to acquire, as well as develop Monte Carlo and gradient-based methods for its efficient estimation. |
Bibtex@inproceedings{ghorbani2019data,
title={Data shapley: Equitable valuation of data for machine learning},
author={Ghorbani, Amirata and Zou, James},
booktitle={International Conference on Machine Learning},
pages={2242--2251},
year={2019},
organization={PMLR}
} |
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| A Distributional Framework for Data Valuation |
Amirata Ghorbani, Michael P. Kim, James Zou |
2020 |
Summary Ghorbani et al. (2020) formulate the Shapley value as a distributional quantity in the context of an underlying data distribution instead of a fixed dataset. They further introduce a novel sampling-based algorithm for the distributional Shapley value with strong approximation guarantees. |
Bibtex@inproceedings{ghorbani2020distributional,
title={A Distributional Framework for Data Valuation},
author={Ghorbani, Amirata, P. Kim, Michael and Zou, James},
booktitle={International Conference on Machine Learning},
year={2020}
} |
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| Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability |
Christopher Frye, Colin Rowat, Ilya Feige |
2020 |
Summary Frye et al. (2020) incorporate causality into the Shapley value framework. Importantly, their framework can handle any amount of causal knowledge and does not require the complete causal graph underlying the data. |
Bibtex@article{frye2020asymmetric,
title={Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability},
author={Frye, Christopher and Rowat, Colin and Feige, Ilya},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
} |
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| Collaborative Machine Learning with Incentive-Aware Model Rewards |
Rachael Hwee Ling Sim, Yehong Zhang, Mun Choon Chan, Bryan Kian Hsiang Low |
2020 |
Summary Sim et al. (2020) introduce a data valuation method with separate ML models as rewards based on the Shapley value and information gain on model parameters given its data. They further define several conditions for incentives such as Shapley fairness, stability, individual rationality, and group welfare, that are suitable for the freely replicable nature of their model reward scheme. |
Bibtex@inproceedings{sim2020collaborative,
title={Collaborative machine learning with incentive-aware model rewards},
author={Sim, Rachael Hwee Ling and Zhang, Yehong and Chan, Mun Choon and Low, Bryan Kian Hsiang},
booktitle={International Conference on Machine Learning},
pages={8927--8936},
year={2020},
organization={PMLR}
} |
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| Validation free and replication robust volume-based data valuation |
Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low |
2021 |
Summary Xu et al. (2021) propose using data diversity via robust volume for measuring the value of data. This removes the need for a validation set and allows for guarantees on replication robustness but suffers from the curse of dimensionality and may ignore useful information in the validation set. |
Bibtex@article{xu2021validation,
title={Validation free and replication robust volume-based data valuation},
author={Xu, Xinyi and Wu, Zhaoxuan and Foo, Chuan Sheng and Low, Bryan Kian Hsiang},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021}
} |
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| Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning |
Yongchan Kwon, James Zou |
2021 |
Summary Kwon & Zou (2022) introduce Beta Shapley, a generalization of Data Shapley by relaxing the efficiency axiom. |
Bibtex@article{kwon2021beta,
title={Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning},
author={Kwon, Yongchan and Zou, James},
journal={arXiv preprint arXiv:2110.14049},
year={2021}
} |
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| Gradient-Driven Rewards to Guarantee Fairness in Collaborative Machine Learning |
Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low |
2021 |
Summary Xu et al. (2021) propose cosine gradient Shapley value to fairly evaluate the expected contribution of each agent's update in the federated learning setting removing the need for an auxiliary validation dataset. They further introduce a novel training-time gradient reward mechanism with a fairness guarantee. |
Bibtex@article{xu2021gradient,
title={Gradient driven rewards to guarantee fairness in collaborative machine learning},
author={Xu, Xinyi and Lyu, Lingjuan and Ma, Xingjun and Miao, Chenglin and Foo, Chuan Sheng and Low, Bryan Kian Hsiang},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={16104--16117},
year={2021}
} |
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| Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning |
Tianhao Wang, Yu Yang, Ruoxi Jia |
2022 |
Summary Wang et al. (2022) propose a general framework to improve effectiveness of sampling-based Shapley value (SV) or Least core (LC) estimation heuristics. They propose learning to predict the performance of a learning algorithm (denoted data utility learning) and using this predictor to estimate learning performance without retraining for cheaper SV and LC estimation. |
Bibtex@article{wang2021improving,
title={Improving cooperative game theory-based data valuation via data utility learning},
author={Wang, Tianhao and Yang, Yu and Jia, Ruoxi},
journal={arXiv preprint arXiv:2107.06336},
year={2021}
} |
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