Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery
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发表时间：
2022-02
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通讯作者：
Quan Nguyen;R. Garnett
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作者：
Quan Nguyen;R. Garnett
主动搜索是自适应实验设计中的一种设置，我们的目标是在预算约束下发现稀有、有价值的类的成员。在这个问题中，一个重要的考虑因素是发现的目标之间的多样性-在许多应用中，不同的发现提供了更多的洞察力，并且在下游任务中可能是优选的。然而，大多数现有的主动搜索策略要么假设所有目标属于一个共同的积极类，或鼓励通过简单的搜索多样性。我们提出了一种新的制定积极的搜索与多个目标类，其特征在于从一个灵活的家庭，其成员鼓励多样性通过收益递减机制的效用函数。然后，我们研究这个问题下的贝叶斯透镜，并证明了一个硬度的结果，逼近任意正，增加，凹效用函数的最优策略。最后，我们设计了一个有效的，非近视近似的最优政策，这类公用事业，并证明其上级经验表现在各种设置，包括药物发现。
Active search is a setting in adaptive experimental design where we aim to uncover members of rare, valuable class(es) subject to a budget constraint. An important consideration in this problem is diversity among the discovered targets -- in many applications, diverse discoveries offer more insight and may be preferable in downstream tasks. However, most existing active search policies either assume that all targets belong to a common positive class or encourage diversity via simple heuristics. We present a novel formulation of active search with multiple target classes, characterized by a utility function chosen from a flexible family whose members encourage diversity via a diminishing returns mechanism. We then study this problem under the Bayesian lens and prove a hardness result for approximating the optimal policy for arbitrary positive, increasing, and concave utility functions. Finally, we design an efficient, nonmyopic approximation to the optimal policy for this class of utilities and demonstrate its superior empirical performance in a variety of settings, including drug discovery.