AI RESEARCH
Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift
arXiv CS.AI
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ArXi:2605.31250v1 Announce Type: cross We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target domain, (2) explaining the shift by identifying the features responsible, and (3) improving the target domain performance. Our method, Entropic Projection Alignment (EPA), aligns the source distribution to the target by matching carefully selected moments while simultaneously minimising the KL divergence from the source.