BibTeX

@article{2405.08719v2,
Author        = {Antoine Wehenkel and Juan L. Gamella and Ozan Sener and Jens Behrmann and Guillermo Sapiro and Jörn-Henrik Jacobsen and Marco Cuturi},
Title         = {Addressing Misspecification in Simulation-based Inference through
  Data-driven Calibration},
Eprint        = {2405.08719v2},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {Driven by steady progress in deep generative modeling, simulation-based
inference (SBI) has emerged as the workhorse for inferring the parameters of
stochastic simulators. However, recent work has demonstrated that model
misspecification can compromise the reliability of SBI, preventing its adoption
in important applications where only misspecified simulators are available.
This work introduces robust posterior estimation~(RoPE), a framework that
overcomes model misspecification with a small real-world calibration set of
ground-truth parameter measurements. We formalize the misspecification gap as
the solution of an optimal transport~(OT) problem between learned
representations of real-world and simulated observations, allowing RoPE to
learn a model of the misspecification without placing additional assumptions on
its nature. RoPE demonstrates how OT and a calibration set provide a
controllable balance between calibrated uncertainty and informative inference,
even under severely misspecified simulators. Results on four synthetic tasks
and two real-world problems with ground-truth labels demonstrate that RoPE
outperforms baselines and consistently returns informative and calibrated
credible intervals.},
Year          = {2024},
Month         = {May},
Url           = {http://arxiv.org/abs/2405.08719v2},
File          = {2405.08719v2.pdf}
}