Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift

2026-05-29

AI Summary

This work addresses three key challenges under distribution shift: performance estimation, attribution-based explanation, and model improvement. To this end, the authors propose the Entropic Projection Alignment (EPA) framework, which simultaneously matches critical moments and minimizes the KL divergence between the labeled source domain and the unlabeled target domain—without requiring explicit density ratio estimation. EPA yields closed-form importance weights and incorporates implicit variance control to enhance robustness. Comprehensive theoretical analysis and extensive experiments demonstrate that EPA significantly outperforms existing methods in estimation accuracy, interpretability, and downstream model performance, while offering computational efficiency and strong theoretical guarantees.

Abstract

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. This formulation yields a unique closed-form solution for importance weights, achieving robustness through implicit variance control. Drawing on domain adaptation theory, we establish that moment matching is sufficient for reliable estimation and adaptation, avoiding the need for full density ratio recovery. Extensive experiments, together with strong theoretical guarantees, demonstrate that EPA consistently outperforms state-of-the-art baselines while offering substantial computational efficiency.

Problem

• distribution shift
• model performance estimation
• feature attribution
• domain adaptation
• importance weighting

Innovation

• Entropic Projection Alignment
• distribution shift
• moment matching
• importance weighting
• domain adaptation