Unsupervised Continual Learning for Amortized Bayesian Inference

• URL: http://arxiv.org/abs/2602.22884v1
• Date: Thu, 26 Feb 2026 11:22:46 GMT
• Title: Unsupervised Continual Learning for Amortized Bayesian Inference
• Authors: Aayush Mishra, Šimon Kucharský, Paul-Christian Bürkner,
• Abstract summary: Amortized Bayesian Inference (ABI) enables efficient posterior estimation using generative neural networks trained on simulated data.<n>Current approaches are limited to static, single-task settings and fail to handle sequentially arriving data or distribution shifts.<n>We propose a continual learning framework for ABI that decouples simulation-based pre-training from unsupervised sequential SC fine-tuning on real-world data.
• Score: 7.052272974286418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Amortized Bayesian Inference (ABI) enables efficient posterior estimation using generative neural networks trained on simulated data, but often suffers from performance degradation under model misspecification. While self-consistency (SC) training on unlabeled empirical data can enhance network robustness, current approaches are limited to static, single-task settings and fail to handle sequentially arriving data or distribution shifts. We propose a continual learning framework for ABI that decouples simulation-based pre-training from unsupervised sequential SC fine-tuning on real-world data. To address the challenge of catastrophic forgetting, we introduce two adaptation strategies: (1) SC with episodic replay, utilizing a memory buffer of past observations, and (2) SC with elastic weight consolidation, which regularizes updates to preserve task-critical parameters. Across three diverse case studies, our methods significantly mitigate forgetting and yield posterior estimates that outperform standard simulation-based training, achieving estimates closer to MCMC reference, providing a viable path for trustworthy ABI across a range of different tasks.

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