KAIROS: Scalable Model-Agnostic Data Valuation

• URL: http://arxiv.org/abs/2506.23799v2
• Date: Wed, 02 Jul 2025 22:50:21 GMT
• Title: KAIROS: Scalable Model-Agnostic Data Valuation
• Authors: Jiongli Zhu, Parjanya Prajakta Prashant, Alex Cloninger, Babak Salimi,
• Abstract summary: KAIROS is a scalable, model-agnostic valuation framework that assigns each example a distributional influence score.<n> KAIROS consistently outperforms state-of-the-art model-, Shapley-, and Wasserstein-based baselines in both accuracy and runtime.
• Score: 8.766103946679435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training data increasingly shapes not only model accuracy but also regulatory compliance and market valuation of AI assets. Yet existing valuation methods remain inadequate: model-based techniques depend on a single fitted model and inherit its biases, while algorithm-based approaches such as Data Shapley require costly retrainings at web scale. Recent Wasserstein-based model-agnostic methods rely on approximations that misrank examples relative to their true leave-one-out (LOO) utility. We introduce KAIROS, a scalable, model-agnostic valuation framework that assigns each example a distributional influence score: its contribution to the Maximum Mean Discrepancy (MMD) between the empirical training distribution and a clean reference set. Unlike Wasserstein surrogates, our MMD-based influence admits a closed-form solution that faithfully approximates the exact LOO ranking within $O(1/N^2)$ error, requires no retraining, and naturally extends to conditional kernels for unified label- and feature-error detection. Moreover, KAIROS supports efficient online updates: when a new batch of size m arrives, all scores can be updated in $O(mN)$ time, delivering up to 50x speedup without compromising ranking quality. Empirical evaluations on noise, mislabeling, and poisoning benchmarks show that KAIROS consistently outperforms state-of-the-art model-, Shapley-, and Wasserstein-based baselines in both accuracy and runtime. We provide rigorous theoretical guarantees, including symmetry for reproducible rankings and density-separation for interpretable thresholds.

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