用无监督域适应提升贝叶斯推断鲁棒性？实验发现效果依赖于错误类型。
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
- 通过域对齐使模拟与真实数据的摘要空间匹配
- 能有效缓解未建模噪声的影响，但会恶化先验错配情况
- 提醒研究者需根据问题类型谨慎选择是否用域适应
神经网络在面对显著偏离训练分布的数据时表现脆弱，尤其在基于模拟的推断方法（如神经网络近似贝叶斯推断，ABI）中，模型在模拟数据上训练后部署到含噪真实观测数据时尤为敏感。近期鲁棒方法采用无监督域适应（UDA）来匹配模拟与观测数据的嵌入空间。然而，缺乏在多种域不匹配场景下的系统评估，使其在高风险应用中的可靠性存疑。本文通过在仿真与真实场景中系统测试多种误设情形，发现对齐不同域的摘要空间可有效缓解未建模现象或噪声的影响。但相同对齐机制在先验误设情况下可能导致失败，这一关键发现具有重要实际意义。结果强调：使用UDA提升ABI鲁棒性时，必须审慎考虑误设类型。
原文摘要 · Abstract (English)
Neural networks are fragile when confronted with data that significantly deviates from their training distribution. This is true in particular for simulation-based inference methods, such as neural amortized Bayesian inference (ABI), where models trained on simulated data are deployed on noisy real-world observations. Recent robust approaches employ unsupervised domain adaptation (UDA) to match the embedding spaces of simulated and observed data. However, the lack of comprehensive evaluations across different domain mismatches raises concerns about the reliability in high-stakes applications. We address this gap by systematically testing UDA approaches across a wide range of misspecification scenarios in silico and practice. We demonstrate that aligning summary spaces between domains effectively mitigates the impact of unmodeled phenomena or noise. However, the same alignment mechanism can lead to failures under prior misspecifications - a critical finding with practical consequences. Our results underscore the need for careful consideration of misspecification types when using UDA to increase the robustness of ABI.
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