考虑网络干扰下的自适应靶向，提升大规模群体学习效率。
Learning to target with network interference
- 基于稀疏线性模型建模个体间干扰，设计可利用网络结构的算法。
- 在不同已知程度下实现近似最优后悔界，证明忽略网络结构会严重低效。
- 适用于社交网络、公共政策等存在溢出效应的场景，尤其适合大规模应用。
本文研究带网络干扰的自适应靶向问题，其中对某个人的干预可能通过溢出效应影响他人。在稀疏线性模型下，每个个体的结果仅受少数其他个体影响。我们首先建立后悔下界，表明忽略网络结构并简化为标准线性贝尔特问题必然导致学习效率低下，尤其在大规模人群中。我们分析了三种干扰结构知识水平：(1) 完全支持已知，(2) 已知列支持大小，(3) 无先验知识。针对每种情形，我们推导了后悔下界，并提出了达到近似最优后悔的算法。结果统一揭示了干扰结构知识如何决定在线学习效率，并提供了各场景下的实用算法。合成与真实数据上的数值实验验证了算法的实际优势。
原文摘要 · Abstract (English)
This paper studies adaptive targeting under network interference in a bandit setting, where treatments applied to one individual may affect others through spillover effects. We consider a linear model in a sparse regime, where each individual's outcome can be affected by at most a few others. We first establish a regret lower bound showing that ignoring the network structure and reducing the problem to a standard linear bandit inevitably leads to inefficient learning, particularly in large populations. To understand how structural information can be leveraged, we analyze regimes with varying levels of knowledge of the interference structure: (1) full support knowledge, (2) knowledge of the column support sizes, and (3) no prior knowledge. For each regime, we establish regret lower bounds characterizing the fundamental limits of learning, and develop algorithms that achieve near-optimal regret. Together, our results provide a unified view of how knowledge of the interference structure governs the efficiency of online learning under interference, and offer practical adaptive targeting algorithms in each setting. Numerical experiments on synthetic and real-world data demonstrate the practical benefits of our algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。