LARP: A Framework for Robust Data Prefiltering Across Multiple Machine Learning Models
Researchers have published a framework called LARP (Learner-Agnostic Robust data Prefiltering) that enables data providers to clean datasets in a way that protects multiple downstream machine learning models simultaneously. The work, published in Transactions on Machine Learning Research, provides theoretical guarantees on worst-case performance loss and empirically measures the trade-off cost across image and tabular tasks. It matters because public datasets increasingly underpin a wide range of AI and statistical systems, and a shared, principled prefiltering approach could reduce redundant data curation efforts across organizations.
The paper formalizes LARP, a prefiltering framework designed to remove low-quality or contaminated samples from public datasets while offering worst-case performance guarantees across a heterogeneous set of downstream learners, rather than optimizing for any single model. The authors establish theoretical feasibility in two settings and quantify what they term the 'price of LARP'—the performance gap incurred by protecting multiple learners simultaneously compared to learner-specific filtering. Empirical evaluations across image classification and tabular learning tasks are used to measure this gap in practice. The paper also introduces a game-theoretic model in which multiple downstream learners can share the cost of a single prefiltering procedure, framing data curation as a cooperative problem. The work was submitted to arXiv in June 2025 and published in Transactions on Machine Learning Research in June 2026.
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- arXiv cs.LGCenter
LARP: Learner-Agnostic Robust Data Prefiltering
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