1. Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
- Author
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Wehenkel, Antoine, Gamella, Juan L., Sener, Ozan, Behrmann, Jens, Sapiro, Guillermo, Cuturi, Marco, and Jacobsen, Jörn-Henrik
- Subjects
Statistics - Machine Learning ,Computer Science - Machine Learning ,Statistics - Methodology - Abstract
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.
- Published
- 2024