Assigning papers to reviewers is a central challenge in the peer-review process of large academic conferences. Program chairs must balance competing objectives, including maximizing reviewer expertise, promoting diversity, and enhancing robustness to strategic manipulation, but it is challenging to do so at the modern conference scale. Existing algorithmic paper assignment approaches either fail to address all of these goals simultaneously or suffer from poor scalability. To address the limitation, we propose Robust Assignment via Marginal Perturbation (RAMP), a unified framework for large-scale peer review. Our approach formulates a linearized perturbed-maximization objective with soft constraints that flexibly balance assignment quality, diversity, and robustness while maintaining runtime efficiency. We further introduce an attribute-aware sampling procedure that converts fractional solutions into integral assignments and improves the diversity and robustness of the final assignment. On datasets with over 20,000 papers and 20,000 reviewers, RAMP runs in under 20 minutes, demonstrating its suitability for real-world deployment.

翻译：将论文分配给审稿人是大型学术会议同行评审过程中的核心挑战。程序委员会主席必须在多个相互竞争的目标之间取得平衡，包括最大化审稿人专业知识、促进多样性以及增强对策略性操纵的鲁棒性，但在现代会议规模下实现这些目标具有挑战性。现有的算法化论文分配方法要么无法同时满足所有这些目标，要么存在可扩展性差的问题。为解决这一局限，我们提出了通过边际扰动实现鲁棒分配（RAMP），这是一个用于大规模同行评审的统一框架。我们的方法构建了一个带有软约束的线性化扰动最大化目标，该目标能在保持运行时效率的同时，灵活地平衡分配质量、多样性和鲁棒性。我们进一步引入了一种属性感知的采样程序，将分数解转换为整数分配，从而提高了最终分配的多样性和鲁棒性。在包含超过20,000篇论文和20,000名审稿人的数据集上，RAMP在20分钟内即可完成运行，证明了其在实际部署中的适用性。