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concept-shift

topic1 events
papersSEP 11 04:00 UTC

New Framework Quantifies Covariate and Concept Shifts in ML Generalization

A new arXiv paper proposes a general approach to measuring how covariate and concept shifts affect machine learning generalization. The authors argue that existing learning bound theory covers only narrow, idealized settings and cannot be estimated from data. Their framework aims to make distribution shift analysis broadly applicable and computable from samples.

research/distribution-shift/generalization/theory