New survey paper outlines machine learning methods for imperfect data

A new survey paper published on arXiv details methods for machine learning when training and testing data are imperfect. The paper categorizes these imperfections into four shared mechanisms: loss of information, biased empirical risk, ambiguous supervision, and unstable representations. It then reviews existing techniques for addressing issues like missing data, imbalanced classes, weak supervision, and domain shifts, concluding with future research directions in evidence-aware learning and uncertainty-preserving prediction. AI

IMPACT Provides a structured overview of techniques for improving machine learning model robustness in real-world, imperfect data scenarios.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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