Amortised and provably-robust simulation-based inference

2026-02-11

AI Summary

This work addresses the challenge of outlier contamination in simulation-based inference caused by instrument malfunctions or human errors. We propose a weighted score matching approach that integrates generalized Bayesian inference with neural network approximations to construct a conditional density estimator that obviates the need for Markov chain Monte Carlo (MCMC) sampling. This method is the first to simultaneously achieve amortized inference and provable robustness in simulation-based inference, substantially simplifying the inference pipeline while maintaining high accuracy and strong resilience to outliers. Notably, it attains these advantages at a computational cost that is only a fraction of that required by current state-of-the-art methods, thereby offering an efficient, accurate, and robust solution for practical applications.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• simulation-based inference
• outliers
• robustness
• extreme values
• faulty measurements

Innovation

• amortised inference
• provably robust
• simulation-based inference
• generalised Bayesian inference
• score-matching