Summary of Addressing Misspecification in Simulation-based Inference Through Data-driven Calibration, by Antoine Wehenkel et al.
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
by Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, Jörn-Henrik Jacobsen
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Robust Posterior Estimation (ROPE), a framework for overcoming model misspecification in Simulation-Based Inference (SBI). Recent work has shown that SBI can be unreliable when models are misspecified. ROPE uses a small calibration set of ground truth parameter measurements to correct for this issue. The authors formalize the misspecification gap as an optimal transport problem and show that their method offers a controllable balance between calibrated uncertainty and informative inference. The results demonstrate that ROPE outperforms baselines on four synthetic tasks and two real-world problems, returning credible intervals that are both informative and well-calibrated. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure that simulations of the world (like computer models) give us accurate answers. Right now, these simulations can be bad if they don’t match what we know to be true. The authors introduce a new way called Robust Posterior Estimation (ROPE) that helps correct for this problem. They use real-world data to make sure the simulations are good and then use that to get more accurate answers. The results show that ROPE works better than other methods on several problems, giving us more reliable answers. |
Keywords
» Artificial intelligence » Inference
