We develop variational search distributions (VSD), a method for finding and generating discrete, combinatorial designs of a rare desired class in a batch sequential manner with a fixed experimental budget. We formalize the requirements and desiderata for active generation and formulate a solution via variational inference. In particular, VSD uses off-the-shelf gradient based optimization routines, can learn powerful generative models for designs, and can take advantage of scalable predictive models. We derive asymptotic convergence rates for learning the true conditional generative distribution of designs with certain configurations of our method. After illustrating the generative model on images, we empirically demonstrate that VSD can outperform existing baseline methods on a set of real sequence-design problems in various biological systems.

翻译：我们提出了变分搜索分布（VSD），这是一种在固定实验预算下，以批量顺序方式寻找并生成属于罕见期望类别的离散组合设计的方法。我们形式化了主动生成的要求与期望，并通过变分推断构建了解决方案。具体而言，VSD利用现成的基于梯度的优化例程，能够学习强大的设计生成模型，并能利用可扩展的预测模型。我们推导了在特定方法配置下学习真实条件生成设计分布的渐近收敛速率。在图像上展示生成模型后，我们通过实验证明，VSD在多个生物系统的一系列真实序列设计问题上能够超越现有的基线方法。