Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
<|reference_start|>Addressing Misspecification in Simulation-based Inference through Data-driven Calibration: Driven by steady progress in generative modeling, simulation-based inference (SBI) has enabled inference over stochastic simulators. However, recent work has demonstrated that model misspecification can harm SBI's reliability. 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 problem between learned representations of real-world and simulated observations. Assuming the prior distribution over the parameters of interest is known and well-specified, our method offers a controllable balance between calibrated uncertainty and informative inference under all possible misspecifications of the simulator. Our empirical results on four synthetic tasks and two real-world problems demonstrate that ROPE outperforms baselines and consistently returns informative and calibrated credible intervals.<|reference_end|>
@article{wehenkel2024addressing,
    title={Addressing Misspecification in Simulation-based Inference through
  Data-driven Calibration},
    author={Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann,
  Guillermo Sapiro, Marco Cuturi, J"orn-Henrik Jacobsen},
    journal={arXiv preprint arXiv:2405.08719},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08719},
    primaryClass={stat.ML cs.LG stat.ME}
}