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REVIEW 3 major objections 5 minor 41 references

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

T0 review · 3 major / 5 minor · reviewed 2026-07-12 · grok-4.5

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Pith's one-line read Even modest noise on the validation set compresses training-data Shapley values toward zero by reshuffling which training points count as neighbors.

desk verdict Real, reproducible KNN-Shapley compression under validation noise; the general-stability claim is still an extrapolation. read the letter →

arxiv 2607.03675 v1 pith:Y6R7ALTH submitted 2026-07-04 cs.LG

Yinan Shen , Ziao Yang , Hongfu Liu This is my paper
classification cs.LG
keywords datavaluationShapleyvalueKNN-Shapleyvalidationsetneighborhoodreshufflingboundarysamplesmarketplacesnoiserobustness
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Shapley values for training data are usually treated as fixed once the training set and model are chosen. This paper shows that assumption fails under ordinary, in-distribution changes to the validation set. Adding feature noise to validation points causes the Shapley values of training points to shrink consistently toward zero. The mechanism is geometric: noise reorders the local neighbors between each validation point and the training set, which flattens the marginal-contribution landscape. Using the efficient KNN-Shapley setting, the authors demonstrate the same directional compression on synthetic Gaussians and six real datasets. They then separate validation points into boundary and non-boundary groups by neighbor-label entropy and apply a simple rescaling that restores spread and positive-count statistics. The result is a practical warning for anyone pricing or selecting data with Shapley values: the numbers are only as stable as the validation geometry that defines them.

What carries the argument

Noise-induced neighborhood reshuffling, observed inside Soft-label KNN-Shapley: perturbations change which training points are the K nearest neighbors of each validation point, which in turn changes every training point's average marginal contribution. Boundary versus non-boundary validation groups (defined by neighbor-label entropy) separate the two main statistical effects—lost spread and lost positive count—so each can be rescaled separately.

What would settle it

Run the same noise-injection protocol with a non-KNN utility (for example exact model-retraining Shapley on a small neural net or logistic regression) and check whether the same directional compression of training-point values and the same boundary/non-boundary split of effects still appear; if they do not, the claimed generality fails.

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Extended reading notes

Core claim

Even modest in-distribution structural changes to the validation set, especially feature noise, produce consistent directional shifts in KNN-Shapley values of training samples, systematically compressing those values toward zero. The driver is a noise-induced neighborhood reshuffling effect that alters the local rank order between validation and training samples and thereby flattens the valuation landscape.

Load-bearing premise

The paper treats the KNN-Shapley geometry with a fixed small neighborhood size and an entropy-based boundary mask as representative enough of Shapley-based data valuation in general that the observed compression and the proposed correction carry over beyond this testbed.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper claims that Shapley values for training data are not stable under fixed training data and model once the validation set is altered by modest in-distribution structural changes (especially additive feature noise). Using Soft-label KNN-Shapley (K=5), the authors show on a 2-D Gaussian-quantiles synthetic set and six real datasets that noise systematically compresses training-sample Shapley values toward zero, reduces their standard deviation, and lowers the count of positive values. They attribute the effect to a noise-induced neighborhood reshuffling that alters local validation-to-training rank order, with distinct roles for boundary versus non-boundary validation points (isolated via one- and two-point case studies). They propose a boundary/non-boundary (B/N) group-wise rescaling plus positive-ratio bias correction (Appendix B) that restores baseline spread and sign structure when a clean reference is available, and discuss entropy-aware normalization and boundary-aware validation design for marketplace robustness.

Significance. If the directional compression and its geometric mechanism hold beyond the KNN setting, the result identifies a previously under-appreciated axis of fragility in data valuation that is directly relevant to collaborative learning, federated settings, and data marketplaces where validation protocols are decentralized or noisy. The empirical consistency across synthetic and real data, the fine-grained boundary/non-boundary case analysis, and the explicit geometric story (neighbor reordering flattening marginal utilities) are genuine strengths. The work does not claim a new algorithm but a diagnostic phenomenon; that framing is useful provided the scope is accurately delimited. The B/N correction, while partly by construction, supplies a concrete, reproducible mitigation template that future work can stress-test.

major comments (3)
  1. [Abstract, §1, §4–§5, §7] Abstract, §1, and §7 frame the result as a general challenge to “the assumption of Shapley stability” and as revealing “a new axis of fragility in data valuation.” All experiments, mechanism analysis (Figs. 1–5), and the B/N correction (Appendix B, Eqs. 6–11) are obtained exclusively with Soft-label KNN-Shapley (K=5). For KNN the utility of a validation point is literally a function of the ordered neighbor list, so any perturbation that reorders that list necessarily changes marginal contributions; the observed compression is therefore almost tautological for this utility class. Without at least one non-neighbor utility (logistic regression, small MLP, or fixed-kernel smoother) the marketplace-facing generality claim over-reaches the evidence. Either restrict the claims to KNN-Shapley or add the missing experiments.
  2. [§6, Appendix B] The proposed correction (Eqs. 3–5 and Appendix B Eqs. 6–11) defines α_B, α_N as ratios of clean-baseline to noisy group standard deviations and chooses the bias b by quantile matching so that the positive fraction exactly recovers the clean baseline ρ_bl. Consequently the restored std and positive-count statistics are recovered largely by construction once a clean reference distribution is known. In the very settings the paper worries about (noisy or adversarially designed validation), that clean baseline is unavailable. The practical utility of the method therefore remains unclear; the manuscript should either supply a baseline-free alternative or explicitly state the strong assumption.
  3. [Appendix B, §5] Boundary membership is defined by an entropy-of-neighbor-labels rule on the K=5 nearest training neighbors (Appendix B), yet no numerical threshold, sensitivity analysis, or ablation on the entropy cutoff appears. Because the entire B/N decomposition and the subsequent group-wise rescaling rest on this binary mask, the load-bearing geometric story is only partially specified. A precise definition and a short sensitivity check are required for reproducibility.
minor comments (5)
  1. [§4, Fig. 1] Figure 1 caption and surrounding text speak of “the cost of data protection,” but the experimental noise is isotropic Gaussian feature noise; the link to privacy mechanisms is never made precise.
  2. [Abstract, §7] The abstract and conclusion mention “entropy-aware normalization” as a mitigation, yet the body only develops the B/N rescaling; either expand the former or remove the claim.
  3. [Appendix A, §6] Table 1 and Appendix A list six real datasets; it would help readers if the main text briefly stated train/val sizes and the exact noise schedule (σ values) used for each.
  4. [§1, §5] Typographical inconsistencies appear (“neighborhood reshuffling as the keymechanism”, “We offeractionsfor fair”, missing spaces after periods in several places).
  5. [§2] References [13] and [11] are central to the KNN-Shapley lineage; a short comparison of how the present inflation/compression phenomenon relates to the inflation bias studied in [13] would strengthen the related-work discussion.

Circularity Check

1 steps flagged · score 5.0 of 10

Main shift phenomenon is empirical and non-circular; B/N correction recovers std and positive-count by explicit construction via α and quantile-matched b

  1. fitted input called prediction [Section 6 Eqs. (3)–(5) and Appendix B Eqs. (8)–(11), Figures 6–8]
    "The group-wise scaling factors are defined by α_G = σ_bl_G / σ_noisy_G … We set b=−Q_{1−ρ_bl}({¯s_i}), ˜s_i = ¯s_i + b. This nearest-quantile matching ensures that the corrected scores recover the baseline fraction of positive Shapley values, while the group-wise scaling restores the spread … Figures 6–8 show that this correction substantially reduces the gap … tracks the baseline much more closely in both standard deviation and positive-count statistics, indicating that the boundary/non-boundary decomposition captures the main direction of the validation-induced shift."

    α_B, α_N and b are constructed precisely so that the corrected group spreads equal the baseline spreads and the positive ratio equals ρ_bl. Reporting recovery of exactly those two statistics is therefore tautological; the figures and the “indicating” claim restate the fitting objectives rather than independently validating the correction.

full rationale

The core claim (noise induces directional compression of KNN-Shapley values via neighborhood reshuffling) is an empirical observation demonstrated on synthetic Gaussian quantiles and six real datasets under Soft-label KNN-Shapley (K=5). It does not reduce by definition to its inputs; the mechanism is diagnosed from case studies of boundary vs non-boundary validation points and is falsifiable. The only circularity appears in the proposed mitigation (Section 6 + Appendix B). There α_B and α_N are defined exactly as the ratios of baseline to noisy group standard deviations, and the additive bias b is chosen by nearest-quantile matching so that the positive fraction of the corrected scores equals the clean baseline ρ_bl. Consequently, the paper’s claim that the correction “tracks the baseline much more closely in both standard deviation and positive-count statistics” is true by construction for those two metrics and does not constitute independent evidence that the B/N decomposition captures the shift. No load-bearing self-citation uniqueness theorem or ansatz smuggling is present; the minor self-citation to the authors’ prior inflation paper is only contextual. Overall circularity is therefore partial and confined to the evaluation of the corrective action.

Assumptions & free parameters 5 free parameters · 4 assumptions · 3 invented entities

The load-bearing content is empirical and geometric rather than axiomatic derivation. The claim rests on the standard Shapley/KNN utility definitions, a hand-chosen neighbor-entropy boundary rule, fixed K, and correction parameters explicitly fitted to restore clean-baseline moments. The main invented conceptual entities are the named phenomenon and the neighborhood-reshuffling mechanism; they are supported only by the paper's own experiments.

free parameters (5)
  • K (nearest neighbors) = 5
    Fixed to K=5 for all experiments and for the entropy boundary mask; changes K would change both valuations and the B/N partition.
  • noise level σ
    Gaussian validation noise scale is varied by the authors to induce the reported shifts; the phenomenon is demonstrated across chosen σ levels rather than derived.
  • group scaling factors α_B, α_N = σ_bl_G / σ_noisy_G
    Defined as σ_bl / σ_noisy for boundary and non-boundary groups so noisy spread matches baseline by construction (Appendix B).
  • bias b (positive-ratio quantile match) = -Q_{1-ρ_bl}({s̄_i})
    Chosen as the negative of the (1-ρ_bl)-quantile of corrected scores so the fraction of positive values equals the clean baseline.
  • boundary fraction λ = n_noisy_B / n_val
    Set to the noisy validation set's boundary fraction and used as the mixture weight between corrected B and N group scores.
assumptions (4)
  • standard math Shapley value of a training point equals its expected marginal utility over coalitions, with utility equal to average negative loss on the validation set.
    Section 3, Eqs. (1)–(2); classical cooperative-game definition imported into data valuation.
  • domain assumption For KNN models, exact or soft-label Shapley values can be computed from neighbor orderings and serve as a faithful testbed for data valuation behavior.
    Section 3 and related work; paper uses Soft-label KNN-SV as computational backbone for all claims about 'Shapley' fragility.
  • ad hoc to paper A validation sample is a boundary sample iff the label entropy of its K nearest training neighbors is high enough to set the binary mask m_j=1.
    Appendix B introduces this entropy rule to partition columns for the B/N correction; it is a design choice of the paper, not a standard theorem.
  • domain assumption In-distribution additive Gaussian feature noise on validation inputs is a representative structural perturbation for studying validation-induced valuation shifts.
    Section 4 operationalizes 'modest changes to the validation set' almost entirely as ϵ~N(0,σ²I) on validation features.
invented entities (3)
  • validation-induced Shapley shifts
    purpose: Name the claimed systematic, directional change in training Shapley distributions caused by validation structure changes.
    Central named phenomenon of the paper; evidence is the authors' own synthetic and real-data experiments.
  • noise-induced neighborhood reshuffling effect
    purpose: Provide the geometric mechanism linking validation perturbations to flattened marginal utilities via reordered local neighbor ranks.
    Introduced in abstract/intro and used to unify boundary vs non-boundary case studies; not independently measured outside this manuscript.
  • boundary-aware B/N correction (group rescaling + positive-ratio bias)
    purpose: Mitigate noisy-validation distortion by separately restoring boundary spread and non-boundary positive mass toward a clean baseline.
    Section 6 and Appendix B define the procedure; success metrics are largely the same statistics used to set α and b.

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Pith. "Pith review of Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation." pith.science (2026). https://pith.science/paper/Y6R7ALTH

@misc{pith2026260703675,
  author       = {Pith},
  title        = {Pith review of: Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y6R7ALTH}},
  note         = {Machine review of arXiv:2607.03675}
}
read the original abstract

Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set. Existing practice often assumes these values are stable once the training data and model are fixed. In this work, we uncover a systematic vulnerability: even modest changes to the validation set, such as introducing noises, cause directional shifts in Shapley distributions. As noises are added, Shapley values of training samples compress toward zero. We trace this to a noise-induced neighborhood reshuffling effect: perturbations alter the local rank order between validation and training samples, flattening the valuation landscape. Using the KNN-Shapley framework, we show through synthetic and real data that these shifts are consistent and reproducible. Our findings challenge the assumption of Shapley stability and reveal a new axis of fragility in data valuation. We propose normalization and boundary-aware validation strategies to mitigate these distortions and enable more robust, interpretable valuation in machine learning marketplaces.

Figures

Figures reproduced from arXiv: 2607.03675 by the authors.

Figure 1
Figure 1. Noise injection demo. A validation sample near the decision boundary is perturbed by Gaussian noise. Colors [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Shapley value shift under the noisy validation set. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Case I with one validation sample. Boundary sample vs non-boundary sample. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Case II with two validation samples [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Case II with two validation samples. the number of training points with net positive value. This mechanism provides a unified explanation for the observed distributional shifts and highlights the critical role of validation-set geometry in shaping data valuation. Our f…
Figure 6
Figure 6. Figure 6: Shapley value correction on the Gaussian dataset. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Shapley value correction on the real-world datasets: Phoneme, Planes2D, and Pol. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Shapley value correction on the real-world datasets: CPU, News20, and CreditCard. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

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