BibTeX

@article{2509.23385v2,
Author        = {Pierre-Louis Ruhlmann and Pedro L. C. Rodrigues and Michael Arbel and Florence Forbes},
Title         = {Flow Matching for Robust Simulation-Based Inference under Model
  Misspecification},
Eprint        = {2509.23385v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Simulation-based inference (SBI) is transforming experimental sciences by
enabling parameter estimation in complex non-linear models from simulated data.
A persistent challenge, however, is model misspecification: simulators are only
approximations of reality, and mismatches between simulated and real data can
yield biased or overconfident posteriors. We address this issue by introducing
Flow Matching Corrected Posterior Estimation (FMCPE), a framework that
leverages the flow matching paradigm to refine simulation-trained posterior
estimators using a small set of real calibration samples. Our approach proceeds
in two stages: first, a posterior approximator is trained on abundant simulated
data; second, flow matching transports its predictions toward the true
posterior supported by real observations, without requiring explicit knowledge
of the misspecification. This design enables FMCPE to combine the scalability
of SBI with robustness to distributional shift. Across synthetic benchmarks and
real-world datasets, we show that our proposal consistently mitigates the
effects of misspecification, delivering improved inference accuracy and
uncertainty calibration compared to standard SBI baselines, while remaining
computationally efficient.},
Year          = {2025},
Month         = {Sep},
Url           = {http://arxiv.org/abs/2509.23385v2},
File          = {2509.23385v2.pdf}
}