arXiv:2605.28516v1 Announce Type: cross Abstract: Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budgets. To address this, we propose DRO-NPE, a distributionally robust approach that replaces the standard NPE objective with a worst-case loss over a Wasserstein ambiguity set. We introduce KL-based metrics for miscoverage and miscalibration, and use these to show that the DRO-NPE objective controls overfitting and reduces posterior overconfidence. Our method is tractable, parallelisable, and readily
The continuous drive for more reliable and efficient AI models, especially in the context of limited data, necessitates advancements in neural posterior estimation. This paper addresses a common limitation in current NPE methods.
Improving the trustworthiness and calibration of AI models, particularly those used for inference, is crucial for their adoption in high-stakes applications. This research offers a method to mitigate overconfidence and unreliability.
The introduction of DRO-NPE provides a more robust and distributionally sound approach to simulation-based inference, potentially leading to more reliable AI predictions with fewer simulation resources.
- · AI researchers
- · Developers of simulation-based inference systems
- · Industries relying on reliable AI models (e.g., finance, healthcare)
- · Systems heavily reliant on uncalibrated NPE without robust training methods
Increased reliability and trustworthiness of AI models in complex simulation environments.
Faster development and deployment of AI systems due to reduced need for extensive simulation budgets.
Broader adoption of AI in critical decision-making processes that currently require high levels of certainty and calibration.
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Read at arXiv cs.LG