---
title: "Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments"
authors:
  - "Danilo Brajovic"
  - "David A. Kreplin"
  - "Marco F. Huber"
doi: "10.24963/ijcai.2026/271"
url: "https://ijcai.org/proceedings/2026/271"
pdf: "https://ijcai.org/proceedings/2026/0271.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "2438-2446"
track: "Main Track"
keywords:
  - "Machine Learning: Other"
  - "Machine Learning: Active learning"
  - "Data Mining: Anomaly/outlier detection"
  - "Machine Learning: Explainable/Interpretable machine learning"
  - "Data Mining: Big data and scalability"
---

# Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments

Danilo Brajovic, David A. Kreplin, Marco F. Huber

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 2438-2446. DOI: [10.24963/ijcai.2026/271](https://doi.org/10.24963/ijcai.2026/271)

## Abstract

Attributing model behavior to training data is an evolving research field. 
A common benchmark is data removal, which involves eliminating data instances with either low or high values, then assessing a model's performance trained on the modified dataset.  Many existing studies leverage Shapley-based data values for this task. In this paper, we demonstrate that these data values are not optimally suited for pruning low-value data when only a limited amount of data remains. To address this limitation, we introduce the Constraint-Data-Value-Maximization (CDVM) approach, which effectively utilizes data attributions for pruning in low-data scenarios. By casting pruning as a constrained optimization that both maximizes total influence and penalizes excessive per-test contributions, CDVM delivers robust performance  when only a small fraction of the data is retained. On the OpenDataVal benchmark, CDVM shows strong performance and competitive runtime.