Distribution alignment can be used to learn invariant representations with applications in fairness and robustness. Most prior works resort to adversarial alignment methods but the resulting minimax problems are unstable and challenging to optimize. Non-adversarial likelihood-based approaches either require model invertibility, impose constraints on the latent prior, or lack a generic framework for alignment. To overcome these limitations, we propose a non-adversarial VAE-based alignment method that can be applied to any model pipeline. We develop a set of alignment upper bounds (including a noisy bound) that have VAE-like objectives but with a different perspective. We carefully compare our method to prior VAE-based alignment approaches both theoretically and empirically. Finally, we demonstrate that our novel alignment losses can replace adversarial losses in standard invariant representation learning pipelines without modifying the original architectures -- thereby significantly broadening the applicability of non-adversarial alignment methods.

通过提出一种基于非对抗的VAE对齐方法，本研究克服了对齐方法中的一些限制，并比较了该方法与先前的基于VAE的对齐方法的理论和实证研究。最终，实验证明我们的新颖对齐损失可以替代标准不变表示学习流程中的对抗损失，从而显著拓宽了非对抗对齐方法的适用范围。

通过可变界实现实用的非对抗性分布对齐