LARP: A Framework for Robust Data Prefiltering Across Multiple Machine Learning Models
Researchers have formalized and analyzed LARP (Learner-Agnostic Robust data Prefiltering), a method for cleaning datasets that works effectively across multiple different machine learning models simultaneously. The approach addresses the problem that public datasets often contain low-quality or contaminated samples that degrade model performance. This matters because it enables data providers to apply a single prefiltering procedure that protects accuracy for diverse downstream applications rather than requiring custom cleaning for each model.
The paper introduces LARP, a principled framework for prefiltering contaminated or low-quality data in public datasets in a way that benefits multiple downstream machine learning models rather than optimizing for a single learner. The researchers establish theoretical guarantees on worst-case loss across a pre-specified set of learners and demonstrate the feasibility of LARP in two theoretical settings. A key finding is that protecting heterogeneous learner sets comes with a performance cost compared to learner-specific prefiltering, which they term the "price of LARP." The authors empirically measure this performance gap across image and tabular datasets and explore cost-sharing benefits through a game-theoretic model where multiple downstream learners can split the expense of a single prefiltering effort.
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- arXiv cs.LGCenter
LARP: Learner-Agnostic Robust Data Prefiltering
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