论文标题
非属性多类主动搜索，并有所减少回报以供多种发现
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery
论文作者
论文摘要
主动搜索是自适应实验设计中的设置，我们旨在发现受预算限制的稀有，有价值的阶级的成员。这个问题的一个重要考虑因素是发现的目标之间的多样性 - 在许多应用中，各种发现提供了更多的见识，并且在下游任务中可能是可取的。但是，大多数现有的主动搜索政策都假定所有目标都属于共同的积极阶级，或者通过简单的启发式方法鼓励多样性。我们提出了一种具有多个目标类别的主动搜索的新表述，其特征是从柔性家庭中选择的效用函数，其成员通过减少回报机制来鼓励多样性。然后，我们研究了贝叶斯镜头下的这个问题，并证明了近似于任意正，增加和凹入效用功能的最佳策略的硬度结果。最后，我们针对该类别的公用事业的最佳政策设计了有效的，非米的近似，并在包括药物发现在内的各种环境中展示了其出色的经验性能。
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.
