深度子模周缘网络
Deep Submodular Peripteral Networks
AI总结：
针对子模函数缺乏实用学习方法、分级成对偏好（GPC）缩放学习研究不足的问题，提出深度子模周缘网络（DSPNs）及配套周缘损失、采样策略，可从GPC中学习子模函数，在实验设计和在线流应用中表现更优。
摘要：
子模函数在各类应用中至关重要，但往往缺乏实用的学习方法来获取这类函数。看似不相关的是，尽管心理测量学领域已有丰富研究历史，但从提供分级成对偏好（GPC）的预言机中学习缩放函数的问题仍未得到充分探索。本文提出了深度子模周缘网络（DSPNs）——一类新型参数化子模函数族，以及基于GPC策略的训练方法，以此关联并解决上述两个挑战。我们设计了全新的GPC风格“周缘”损失，利用对象对（本文中为集合对）之间的数值分级关系。与传统对比学习或RLHF偏好排序不同，我们的方法采用分级比较，能提取比二元结果比较更精细的信息，且可对比任意大小的集合（而非仅两个集合）。我们还定义了一套全新的自动采样训练策略，包括受主动学习启发的子模反馈机制。实验验证了DSPNs在从高成本目标子模函数中学习子模性的有效性，并展现了其在实验设计和在线流应用中的优越性。
英文摘要：
Submodular functions, crucial for various applications, often lack practical learning methods for their acquisition. Seemingly unrelated, learning a scaling from oracles offering graded pairwise preferences (GPC) is underexplored, despite a rich history in psychometrics. In this paper, we introduce deep submodular peripteral networks (DSPNs), a novel parametric family of submodular functions, and methods for their training using a GPC-based strategy to connect and then tackle both of the above challenges. We introduce newly devised GPC-style ``peripteral'' loss which leverages numerically graded relationships between pairs of objects (sets in our case). Unlike traditional contrastive learning, or RHLF preference ranking, our method utilizes graded comparisons, extracting more nuanced information than just binary-outcome comparisons, and contrasts sets of any size (not just two). We also define a novel suite of automatic sampling strategies for training, including active-learning inspired submodular feedback. We demonstrate DSPNs' efficacy in learning submodularity from a costly target submodular function and demonstrate its superiority both for experimental design and online streaming applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
