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Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

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arxiv 2110.14049 v2 pith:ZM4K4C55 submitted 2021-10-26 cs.LG stat.ML

Yongchan Kwon , James Zou This is my paper
classification cs.LGstat.ML
keywords shapleydatabetalearningmachineseveralvaluationframework
verification ladder T0 review T1 audit T2 compute T3 formal
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Data Shapley has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. It can effectively identify helpful or harmful data points for a learning algorithm. In this paper, we propose Beta Shapley, which is a substantial generalization of Data Shapley. Beta Shapley arises naturally by relaxing the efficiency axiom of the Shapley value, which is not critical for machine learning settings. Beta Shapley unifies several popular data valuation methods and includes data Shapley as a special case. Moreover, we prove that Beta Shapley has several desirable statistical properties and propose efficient algorithms to estimate it. We demonstrate that Beta Shapley outperforms state-of-the-art data valuation methods on several downstream ML tasks such as: 1) detecting mislabeled training data; 2) learning with subsamples; and 3) identifying points whose addition or removal have the largest positive or negative impact on the model.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Asymptotic Analysis of the Shapley Value for Dataset Valuation

    cs.GT 2026-07 conditional novelty 7.0 of 10

    Under smooth RKHS embedding utilities, a fixed owner's Shapley value is O(1/I)-close in L1 to an explicit leading term of scale (log I)/I driven by a first-order population signal.

  2. Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation

    cs.LG 2026-07 conditional novelty 5.0 of 10

    In-distribution validation noise directionally compresses KNN-Shapley values of training samples toward zero via neighborhood reshuffling, and a boundary-aware rescaling can partially restore baseline statistics.

  3. KAIROS: Scalable Model-Agnostic Data Valuation

    cs.LG 2025-06 conditional novelty 5.0 of 10

    KAIROS derives a closed-form Maximum Mean Discrepancy influence score that approximates leave-one-out data rankings and detects noise, mislabels, and backdoors without retraining.

  4. Semivalue-based data valuation is arbitrary and gameable

    cs.LG 2025-06 conditional novelty 5.0 of 10

    Semivalue-based data valuations are shown to be highly sensitive to plausible utility-function choices and are gameable under the paper's weak definition of gameability.

  5. In-Run Data Shapley for Adam Optimizer

    cs.LG 2026-01 reject novelty 4.0 of 10

    An 'Adam-aware' approximation for In-Run Data Shapley is proposed, but its central theorem is not proven and the fidelity test uses a proxy that is not the true Shapley value.

  6. On the Usage of Gaussian Process for Efficient Data Valuation

    cs.LG 2025-06 conditional novelty 3.0 of 10

    A Gaussian Process integrated-variance utility, updated via Schur complements, computes Shapley data values with less retraining, but the method is numerically unstable in practice.

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