Inductive Domain Transfer In Misspecified Simulation-Based Inference

• URL: http://arxiv.org/abs/2508.15593v3
• Date: Tue, 21 Oct 2025 15:45:00 GMT
• Title: Inductive Domain Transfer In Misspecified Simulation-Based Inference
• Authors: Ortal Senouf, Antoine Wehenkel, Cédric Vincent-Cuaz, Emmanuel Abbé, Pascal Frossard,
• Abstract summary: We propose a fully inductive and amortized SBI framework that integrates calibration and distributional alignment into a single, end-to-end trainable model.<n>Our approach matches or surpasses the performance of RoPE, as well as other standard SBI and non- SBI estimators.
• Score: 29.26298096319145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation-based inference (SBI) is a statistical inference approach for estimating latent parameters of a physical system when the likelihood is intractable but simulations are available. In practice, SBI is often hindered by model misspecification--the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, a recent SBI approach, addresses this challenge through a two-stage domain transfer process that combines semi-supervised calibration with optimal transport (OT)-based distribution alignment. However, RoPE operates in a fully transductive setting, requiring access to a batch of test samples at inference time, which limits scalability and generalization. We propose here a fully inductive and amortized SBI framework that integrates calibration and distributional alignment into a single, end-to-end trainable model. Our method leverages mini-batch OT with a closed-form coupling to align real and simulated observations that correspond to the same latent parameters, using both paired calibration data and unpaired samples. A conditional normalizing flow is then trained to approximate the OT-induced posterior, enabling efficient inference without simulation access at test time. Across a range of synthetic and real-world benchmarks--including complex medical biomarker estimation--our approach matches or surpasses the performance of RoPE, as well as other standard SBI and non-SBI estimators, while offering improved scalability and applicability in challenging, misspecified environments.

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