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[2602.11325] Amortised and provably-robust simulation-based inference
Summary
This paper presents a novel method for simulation-based inference that is robust to outliers and simplifies computation by eliminating the need for Markov chain Monte Carlo sampling.
Why It Matters
The proposed approach addresses a significant limitation in existing simulation-based inference methods, which often struggle with outliers. By providing a robust solution, this research enhances the reliability of inference in various scientific and engineering applications, potentially leading to more accurate results in critical fields.
Key Takeaways
- Introduces a robust simulation-based inference method.
- Eliminates the need for complex Markov chain Monte Carlo sampling.
- Utilizes generalized Bayesian inference for improved accuracy.
- Demonstrates significant computational advantages over existing methods.
- Addresses outlier sensitivity in data from faulty measurements.
Statistics > Machine Learning arXiv:2602.11325 (stat) [Submitted on 11 Feb 2026 (v1), last revised 17 Feb 2026 (this version, v2)] Title:Amortised and provably-robust simulation-based inference Authors:Ayush Bharti, Charita Dellaporta, Yuga Hikida, François-Xavier Briol View a PDF of the paper titled Amortised and provably-robust simulation-based inference, by Ayush Bharti and 3 other authors View PDF HTML (experimental) Abstract:Complex simulator-based models are now routinely used to perform inference across the sciences and engineering, but existing inference methods are often unable to account for outliers and other extreme values in data which occur due to faulty measurement instruments or human error. In this paper, we introduce a novel approach to simulation-based inference grounded in generalised Bayesian inference and a neural approximation of a weighted score-matching loss. This leads to a method that is both amortised and provably robust to outliers, a combination not achieved by existing approaches. Furthermore, through a carefully chosen conditional density model, we demonstrate that inference can be further simplified and performed without the need for Markov chain Monte Carlo sampling, thereby offering significant computational advantages, with complexity that is only a small fraction of that of current state-of-the-art approaches. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO); Methodology (stat.ME) Cite as: arXiv:2602...
