OpenNovelty.org

On the Impact of the Utility in Semivalue-based Data Valuation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Data valuationSemivalueUtilityRobustness
Abstract:

Semivalue–based data valuation uses cooperative‐game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the practitioner’s choice of utility, raising the question: How robust is semivalue-based data valuation to changes in the utility? This issue is critical when the utility is set as a trade‐off between several criteria and when practitioners must select among multiple equally valid utilities. We address it by introducing the notion of a dataset’s spatial signature: given a semivalue, we embed each data point into a lower-dimensional space where any utility becomes a linear functional, making the data valuation framework amenable to a simpler geometric picture. Building on this, we propose a practical methodology centered on an explicit robustness metric that informs practitioners whether and by how much their data valuation results will shift as the utility changes. We validate this approach across diverse datasets and semivalues, demonstrating strong agreement with rank‐correlation analyses and offering analytical insight into how choosing a semivalue can amplify or diminish robustness.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces a spatial signature framework to analyze how semivalue-based data values shift when utility functions change. It resides in the 'Utility Function Robustness and Arbitrariness' leaf, which contains only three papers total, including this work and two siblings examining semivalue arbitrariness and Shapley arbitrariness. This leaf sits within the broader 'Theoretical Foundations and Sensitivity Analysis' branch, indicating the paper addresses a core theoretical concern in a relatively sparse research direction focused specifically on utility function sensitivity.

The taxonomy reveals neighboring work in 'Sensitivity Bounds and Stability Analysis' (two papers on formal guarantees under perturbations) and application-oriented branches covering privacy-preserving methods and domain-specific valuation. The paper's geometric embedding approach diverges from sibling studies that emphasize non-uniqueness or arbitrariness of semivalues without proposing unified geometric models. Its focus on explicit robustness metrics bridges theoretical sensitivity analysis and practical guidance, connecting to but distinct from distributional robustness frameworks found in the Extensions branch.

Among twenty-four candidates examined, the spatial signature contribution (ten candidates, zero refutations) and robustness metric contribution (seven candidates, zero refutations) appear novel within this limited search scope. The analytical insights into semivalue robustness differences (seven candidates, one refutation) show some prior overlap, suggesting existing work may have explored how different semivalues amplify or diminish sensitivity. The search scale indicates focused examination of closely related literature rather than exhaustive coverage, leaving open the possibility of additional relevant work beyond top semantic matches.

Given the sparse taxonomy leaf and limited refutations across most contributions, the paper appears to occupy a relatively underexplored niche within semivalue robustness analysis. The geometric modeling perspective and explicit robustness quantification distinguish it from sibling arbitrariness studies, though the analytical insights component shows measurable prior work. This assessment reflects findings from thirty candidate papers and may not capture all relevant literature in adjacent subfields or recent preprints.

Taxonomy

Core-task Taxonomy Papers
7
3
Claimed Contributions
24
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: robustness of semivalue-based data valuation to utility function changes. The field of data valuation has grown around the idea of assigning worth to individual training examples, often using cooperative game theory concepts such as the Shapley value and its generalizations (semivalues). The taxonomy reflects two main branches: Theoretical Foundations and Sensitivity Analysis, which examines how stable these valuations are under perturbations or modeling choices, and Extensions and Applications, which adapts semivalue methods to specialized domains like privacy-preserving settings or graph-structured data. Within the theoretical branch, a key concern is understanding how arbitrary or fragile valuation scores can be when the underlying utility function—often a performance metric or model accuracy measure—is altered. Works like Semivalue Arbitrariness[1] and Shapley Arbitrariness[3] directly probe this sensitivity, while others such as Distributionally Robust Valuation[2] propose frameworks to mitigate instability by considering worst-case or distributional shifts in utility. A particularly active line of inquiry focuses on whether semivalue-based scores remain meaningful when utility functions change, either due to different evaluation metrics or noisy estimates. Utility Impact Semivalue[0] sits squarely in this cluster, investigating the extent to which semivalue rankings hold up under utility perturbations and offering theoretical or empirical bounds on their robustness. This contrasts with neighboring studies like Semivalue Arbitrariness[1], which may emphasize the inherent non-uniqueness of semivalues more broadly, and Shapley Arbitrariness[3], which zeroes in on the classical Shapley case. Meanwhile, application-oriented extensions—such as Privacy Friendly Valuation[4], Graph Data Valuation[5], and Classwise Shapley[6]—demonstrate that robustness questions remain relevant even when valuation methods are tailored to specific data modalities or privacy constraints. Overall, the landscape reveals an ongoing tension between the theoretical appeal of game-theoretic fairness and the practical need for stable, interpretable scores across varying utility definitions.

Claimed Contributions

Unified geometric modeling via spatial signature

The authors introduce the notion of a dataset's spatial signature, which embeds each data point into a lower-dimensional space where any utility becomes a linear functional. This geometric representation unifies both the utility trade-off scenario and the multiple-valid-utility scenario, enabling a simpler geometric interpretation of semivalue-based data valuation.

10 retrieved papers
Robustness metric derived from geometric representation

The authors propose a practical robustness metric Rp that quantifies how stable semivalue-based data value rankings remain as the utility function changes. This metric is derived from the spatial signature and measures the minimal angular distance required to induce a specified number of pairwise swaps in the ranking.

7 retrieved papers
Analytical insights into semivalue robustness differences

The authors provide analytical insights explaining why Banzhaf achieves higher robustness than other semivalues. They show that Banzhaf's weighting scheme tends to collinearize the spatial signature, which geometrically explains its greater stability under utility shifts.

7 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unified geometric modeling via spatial signature

The authors introduce the notion of a dataset's spatial signature, which embeds each data point into a lower-dimensional space where any utility becomes a linear functional. This geometric representation unifies both the utility trade-off scenario and the multiple-valid-utility scenario, enabling a simpler geometric interpretation of semivalue-based data valuation.

Contribution

Robustness metric derived from geometric representation

The authors propose a practical robustness metric Rp that quantifies how stable semivalue-based data value rankings remain as the utility function changes. This metric is derived from the spatial signature and measures the minimal angular distance required to induce a specified number of pairwise swaps in the ranking.

Contribution

Analytical insights into semivalue robustness differences

The authors provide analytical insights explaining why Banzhaf achieves higher robustness than other semivalues. They show that Banzhaf's weighting scheme tends to collinearize the spatial signature, which geometrically explains its greater stability under utility shifts.

Table of Contents