DeMix: New Framework for Identifying and Classifying Training Data Errors in Machine Learning
Researchers have developed DeMix, a framework that automatically detects erroneous training data samples and classifies the type of error (label errors, feature errors, or spurious correlations) they contain. The method uses influence vectors to capture how each training sample affects model predictions, then applies multi-label classification to identify error types. The approach showed significant improvements over existing methods, with 22.61% better performance in data debugging and 9.32% improvement in downstream task performance after repair.
DeMix addresses a critical challenge in machine learning: training datasets often contain mixed types of errors from data preparation pipelines, but existing methods struggle to both detect these errors and classify their specific types for targeted repair. The framework's key innovation is using influence vectors—mathematical representations of how each training sample affects model predictions across validation samples—to capture error-specific patterns. DeMix formulates data debugging as a multi-label classification problem and introduces an intervention-based learning strategy to ensure the classifier learns invariant patterns specific to each error type. Empirical evaluation across 11 diverse tasks (tabular data, recommendation systems, and LLM alignment) demonstrated substantial improvements: 22.61% gain in F1-score for data debugging and 9.32% improvement in final model performance after data repair. The authors have made code publicly available, supporting reproducibility and adoption.
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- arXiv cs.LGCenter
DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors
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