Towards Practical Non-Adversarial Distribution Matching
This addresses the optimization challenges in distribution matching for fairness and robustness applications, offering a more stable alternative to adversarial methods.
The paper tackled the problem of unstable adversarial methods in distribution matching for invariant representation learning by proposing a non-adversarial VAE-based method with alignment upper bounds, demonstrating that it can replace adversarial losses in standard pipelines without architectural changes.
Distribution matching can be used to learn invariant representations with applications in fairness and robustness. Most prior works resort to adversarial matching 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 distribution matching. To overcome these limitations, we propose a non-adversarial VAE-based matching method that can be applied to any model pipeline. We develop a set of alignment upper bounds for distribution matching (including a noisy bound) that have VAE-like objectives but with a different perspective. We carefully compare our method to prior VAE-based matching approaches both theoretically and empirically. Finally, we demonstrate that our novel matching losses can replace adversarial losses in standard invariant representation learning pipelines without modifying the original architectures -- thereby significantly broadening the applicability of non-adversarial matching methods.