DABS高效筛选高维离散设计中关键因子，适合实验预算紧张场景。
Deep Adaptive Bayesian Screening
- 用策略网络学习逐轮选实验，降低贝叶斯实验设计成本。
- 在有限实验次数下，准确率优于经典与贝叶斯基线方法。
- 支持稀疏性与交互作用建模，适合真实世界高维筛选任务。
我们提出深奥自适应贝叶斯筛选（DABS），用于高维离散设计空间中的自适应因子筛选。DABS离线训练一个策略网络，以序列方式选择信息量大的实验，实现贝叶斯最优实验设计的摊销。该方法处理二值设计，通过带强遗传性的尖刺-平滑先验建模稀疏性和交互作用。模型利用对比下界进行训练，其中混淆效应大小和噪声方差被解析积分掉。与以往摊销贝叶斯设计方法不同，DABS在部署时还集成吉布斯后验推断，可输出因子活动的后验概率及效应大小的可信区间。我们在符合真实世界基准的筛选问题上验证了DABS，结果表明其在严格实验预算下，相较经典和贝叶斯基线方法具有更优的准确性和可扩展性。
原文摘要 · Abstract (English)
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.
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