通过熵投影对齐，统一解决分布偏移下的性能估计、原因解释和模型改进。
Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift
- 基于关键矩匹配与KL散度最小化，实现源域到目标域的高效对齐。
- 在多个数据集上优于现有方法，且计算效率显著提升。
- 适合需要可靠迁移性能评估与解释的机器学习应用。
我们提出一个统一框架，应对分布偏移的三大挑战：(1) 估计模型在未标注目标域上的性能，(2) 通过识别相关特征解释分布偏移，(3) 改善目标域性能。所提方法熵投影对齐（EPA）通过匹配精心选择的矩，同时最小化源域到目标域的KL散度，实现源分布与目标分布的对齐。该公式推导出重要性权重的唯一闭式解，通过隐式方差控制实现鲁棒性。基于领域自适应理论，我们证明矩匹配足以实现可靠的性能估计与适应，无需完整密度比恢复。大量实验结合强理论保证表明，EPA在多个基准上持续优于先进方法，且具备显著计算效率优势。
原文摘要 · Abstract (English)
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.
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