arXiv:2506.20573v4 Announce Type: replace-cross Abstract: Public datasets, crucial for modern machine learning and statistical inference, often contain low-quality or contaminated samples that can harm model performance. This creates a need for principled prefiltering procedures that a data provider can apply to protect the accuracy of a range of potential downstream statistical and learning procedures simultaneously. In this work, we formalize and analyze Learner-Agnostic Robust data Prefiltering (LARP), the problem of designing prefiltering procedures with guarantees on the worst-case loss o
The increasing reliance on public datasets for AI training, combined with growing awareness of data quality issues, necessitates advanced prefiltering techniques.
Ensuring data quality translates directly to more robust and reliable AI models, critical for high-stakes applications and efficient resource allocation in ML development.
The formalization of Learner-Agnostic Robust data Prefiltering (LARP) offers a standardized, principled approach to data sanitization, applicable across diverse machine learning tasks.
- · AI developers
- · Data providers
- · ML model users
- · Developers neglecting data quality
- · Low-quality data aggregators
Improved performance and reliability of AI systems, reducing the incidence of 'garbage in, garbage out' failures.
Increased trust in public datasets and AI-driven insights, potentially accelerating AI adoption in sensitive sectors.
Standardization of data prefiltering could lead to new industry certifications or regulatory requirements for AI data quality.
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Read at arXiv cs.LG