Abstract:Simulation-based inference (SBI) provides amortized Bayesian parameter inference from simulator-generated data without requiring explicit likelihood evaluation. Its reliability can degrade under model misspecification, where real-world observations are not well represented by the simulator used for training. Existing methods using unlabeled real-world data often align simulated and real-world data distributions, but marginal alignment alone does not directly preserve parameter-relevant information needed for posterior inference. We propose SPIN, an SBI framework with parameter-relevant information-preserving domain transfer using unlabeled, unpaired real-world observations. During training, SPIN translates labeled simulator observations toward the real-world domain and back to the simulator domain, using the original simulator labels to encourage domain transfer that preserves parameter-relevant mutual information. At test time, the learned real-to-simulator transport maps real-world observations into the simulator domain for posterior inference, without requiring real-world parameter labels or paired real--simulator observations. Across controlled synthetic and physical real-world benchmarks, SPIN improves real-world posterior inference, with the improvement becoming clearer as misspecification increases.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.05652 [cs.LG] |
| (or arXiv:2605.05652v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.05652 arXiv-issued DOI via DataCite (pending registration) |
From: Joon Jang [view email]
[v1]
Thu, 7 May 2026 04:06:53 UTC (1,115 KB)
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