{"type":"ScholarlyArticle","title":"Constraint-Data-Value-Maximization: Utilizing Data Attribution for Effective Data Pruning in Low-Data Environments","authors":[{"name":"Danilo Brajovic","slug":"danilo-brajovic","given":"Danilo","family":"Brajovic","affiliation":""},{"name":"David A. Kreplin","slug":"david-a-kreplin","given":"David A.","family":"Kreplin","affiliation":""},{"name":"Marco F. Huber","slug":"marco-f-huber","given":"Marco F.","family":"Huber","affiliation":""}],"abstract":"Attributing model behavior to training data is an evolving research field. \r\nA 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.","doi":"10.24963/ijcai.2026/271","url":"https://ijcai.org/proceedings/2026/271","pdf":"https://ijcai.org/proceedings/2026/0271.pdf","markdown":"https://ijcai.org/proceedings/2026/271.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/271","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":271,"pages":{"first":2438,"last":2446},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}