Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferenceDomain AdaptationUnsupervised Domain AdaptationSimilar Papers 제목 키워드 기반
Unsupervised Robust Domain Adaptation: Paradigm, Theory and Algorithm
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain by addressing domain shifts. Most UDA approaches emphasize transfer ability, but often overloo…
Unsupervised Domain AdaptationAdversarial RobustnessGeoNet: Benchmarking Unsupervised Adaptation across Geographies
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training. An important practical problem pertains to models deployed in a new geogra…
BenchmarkingDomain Adaptationimage-classificationImage Classification+2Target Consistency for Domain Adaptation: when Robustness meets Transferability
Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation. By investigating the robustness of such methods under the prism of the clu…
Domain Adaptationimage-classificationImage ClassificationUnsupervised Domain AdaptationAdversarial Robustness for Unsupervised Domain Adaptation
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models. However…
Adversarial RobustnessDomain AdaptationUnsupervised Domain AdaptationTowards Accurate and Robust Domain Adaptation under Noisy Environments
In non-stationary environments, learning machines usually confront the domain adaptation scenario where the data distribution does change over time. Previous domain adaptation works have achieved great success in theory …
Domain AdaptationUnsupervised Domain Adaptation