BibTeX

@article{2605.05652v1,
Author        = {Joon Jang and Eunho Jeong and Kyu Sung Choi and Hyeonjin Kim},
Title         = {Information-Preserving Domain Transfer with Unlabeled Data in Misspecified Simulation-Based Inference},
Eprint        = {2605.05652v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
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.},
Year          = {2026},
Month         = {May},
Url           = {http://arxiv.org/abs/2605.05652v1},
File          = {2605.05652v1.pdf}
}