Amortized variational inference produces a posterior approximator that can compute a posterior approximation given any new observation. Unfortunately, there are few guarantees about the quality of these approximate posteriors. We propose Conformalized Amortized Neural Variational Inference (CANVI), a procedure that is scalable, easily implemented, and provides guaranteed marginal coverage. Given a collection of candidate amortized posterior approximators, CANVI constructs conformalized predictors based on each candidate, compares the predictors using a metric known as predictive efficiency, and returns the most efficient predictor. CANVI ensures that the resulting predictor constructs regions that contain the truth with high probability (exactly how high is prespecified by the user). CANVI is agnostic to design decisions in formulating the candidate approximators and only requires access to samples from the forward model, permitting its use in likelihood-free settings. We prove lower bounds on the predictive efficiency of the regions produced by CANVI and explore how the quality of a posterior approximation relates to the predictive efficiency of prediction regions based on that approximation. Finally, we demonstrate the accurate calibration and high predictive efficiency of CANVI on a suite of simulation-based inference benchmark tasks and an important scientific task: analyzing galaxy emission spectra.

CANVI是一种可扩展、易于实施且具有保证边际覆盖率的方法，可以通过对每个候选者构建经过确认的预测模型、比较预测效率并返回最有效的预测器来构建后验概率逼近程序，同时不需要对候选逼近器的设计决策进行考虑，在没有似然函数的情况下使用CANVI，我们对CANVI所产生的预测区域的预测效率进行了下界证明，并探讨了后验概率逼近质量与基于该逼近的预测区域的预测效率之间的关系，最后，在一系列基于模拟的推断基准任务以及一个重要的科学任务（分析恒星发射光谱）中，我们展示了CANVI的精确校准和高预测效率。

具有覆盖保障的变分推断