Deep Adaptive Bayesian Screening
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 ...
| Authors | Jade Lejeune Herman, Arno Strouwen, Johan A. K. Suykens et al. |
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| Primary category | stat.ML |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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