covariate-shift

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papersTODAY 04:00 UTC

Density Ratio Estimation and Importance-Weighted Regression Under Target Shift

This paper examines how to estimate density ratios and perform importance-weighted regression when the output distribution shifts between training and test data while the conditional input distribution given outputs stays the same. The authors derive optimal estimation approaches for this continuous-output target shift setting. The work falls in the statistical machine learning area of distribution shift and covariate/label shift correction.

research/machine-learning/distribution-shift/statistics

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