发表机构
University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合扩散模型引导与目标最大似然估计的多保真度方法，融合高低分辨率模拟以解决MNAR选择偏差问题，降低计算成本并提升目标参数估计准确性。
AI 中文摘要
我们开发了一种多保真度方法，用于融合低分辨率模拟与计算成本高昂、运行频率低且易产生偏差的高分辨率模拟。我们将该融合问题表述为非随机缺失（MNAR）选择偏差下的约束优化问题，该公式旨在寻找指数倾斜的高分辨率分布，使其与有偏差基线的KL散度最小化，且需满足源自低分辨率模拟的矩约束。该优化首先需要将有偏差基线条件密度f作为讨厌参数进行估计，我们采用基于得分的扩散模型来估计f。为消除生成模型的正则化偏差（该偏差会损害下游任务），我们应用目标最大似然估计（TMLE），TMLE通过目标指数倾斜对f进行去偏处理，使目标参数对一阶讨厌参数估计误差不敏感。为实现该计算，我们采用了最初为人类偏好对齐开发的生成模型引导技术，使用基于我们公式的奖励函数的Feynman-Kac引导，在推理时同时执行MNAR和TMLE的指数倾斜，避免了高昂的再训练成本。代码可在此处获取：this https URL
英文摘要
We develop a multifidelity method for fusing low-resolution simulations with computationally expensive high-resolution simulations, which are run infrequently and are therefore prone to bias. We formulate this fusion as a constrained optimization under missing-not-at-random (MNAR) selection bias. This formulation searches for the exponentially tilted high-resolution distribution that minimizes KL divergence from the biased baseline, subject to moment constraints derived from low-resolution simulations. This optimization requires first estimating the biased baseline conditional density $f$ as a nuisance parameter. We estimate $f$ using a score-based diffusion model. To eliminate the generative model's regularization bias that harms the downstream task, we apply targeted maximum likelihood estimation (TMLE). TMLE debiases $\hat{f}$ via a targeted exponential tilting, rendering the target parameters insensitive to first-order nuisance estimation errors. To execute this computationally, we adapt generative model steering, a technique originally developed for human-preference alignment. Using Feynman-Kac steering with a reward function based on our formulation, we simultaneously execute the exponential tilts for MNAR and TMLE at inference time, avoiding expensive retraining costs. Code available [here](https://github.com/Jong-Min-Moon/multifidel_emul_by_FK).
Comments27 pages, 1 table, 4 algorithms
