BibTeX

@article{2508.15593v2,
Author        = {Ortal Senouf and Cédric Vincent-Cuaz and Emmanuel Abbé and Pascal Frossard},
Title         = {Inductive Domain Transfer In Misspecified Simulation-Based Inference},
Eprint        = {2508.15593v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {cs.LG},
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.},
Year          = {2025},
Month         = {Aug},
Url           = {http://arxiv.org/abs/2508.15593v2},
File          = {2508.15593v2.pdf}
}