发表机构
KU Leuven（卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在高维离散设计空间的自适应因子筛选问题，提出深度自适应贝叶斯筛选方法DABS，通过学习策略网络选择实验，集成吉布斯后验推断，相比经典和贝叶斯基线，在实验预算紧张时准确性和可扩展性更优。
AI 中文摘要
我们介绍了深度自适应贝叶斯筛选（DABS），这是一种在高维离散设计空间中进行自适应因子筛选的方法。DABS离线学习策略网络以顺序选择信息丰富的实验，摊销贝叶斯最优实验设计。它处理二元设计，通过具有强遗传性的尖峰平板先验纳入稀疏性和相互作用。该模型使用关于因子活性信息的对比下限进行训练，分析性地整合出干扰效应大小和噪声方差。与先前的摊销贝叶斯设计方法不同，DABS在部署时还集成了吉布斯后验推断，产生因子活性的后验概率和效应大小的可信区间。我们在针对现实世界基准校准的筛选问题上展示了DABS，并表明在严格的实验预算下，它比经典和贝叶斯基线具有更高的准确性和可扩展性。
英文摘要
We introduce Deep Adaptive Bayesian Screening (DABS), a method for performing adaptive factorial screening in high-dimensional discrete design spaces. DABS learns a policy network offline to sequentially select informative experiments, amortizing Bayesian Optimal Experimental Design. It handles binary designs, incorporates sparsity and interactions via a spike-and-slab prior with strong heredity. The model is trained using a contrastive lower bound on information about factor activity with nuisance effect sizes and noise variance analytically integrated out. Unlike prior amortized Bayesian design approaches, DABS also integrates Gibbs posterior inference at deployment, yielding posterior probabilities of factor activity and credible intervals on effect sizes. We demonstrate DABS on screening problems calibrated to real-world benchmarks and show it achieves superior accuracy and scalability over classical and Bayesian baselines under tight experimental budgets.
