优化论文拒稿策略，11年ICLR数据验证可减少近20%无意义拒稿
Accept More, Reject Less: Reducing up to 19% Unnecessary Desk-Rejections over 11 Years of ICLR Data
- 基于线性规划与取整方案，重新设计拒稿顺序以提升公平性
- 在不突破作者限投数前提下，最多多保留19.23%的可接受论文
- 算法高效，全部计算耗时不超过53.64秒，适合实际会议应用
AI研究爆炸式增长导致顶会投稿量激增，2025年多个会议（如CVPR、ICCV、KDD、AAAI、IJCAI、WSDM）实施作者投稿上限并按提交顺序进行简单拒稿。该政策虽减轻审稿负担，却可能误删优质论文并惩罚作者努力。本文提出能否在遵守投稿限制的同时最小化无意义拒稿。我们首先将现有拒稿策略形式化为优化问题，进而设计一种基于线性规划松弛与取整方案的实用算法。在11年真实ICLR数据上评估，该方法在不违反作者投稿限制的前提下，最多可多保留19.23%的论文。此外，算法实践效率极高，所有结果均在53.64秒内完成。本工作提供了一种简单可行的拒稿策略，显著减少无效拒稿，具有改进当前计算机科学会议投稿制度的巨大潜力。
原文摘要 · Abstract (English)
The explosive growth of AI research has driven paper submissions at flagship AI conferences to unprecedented levels, necessitating many venues in 2025 (e.g., CVPR, ICCV, KDD, AAAI, IJCAI, WSDM) to enforce strict per-author submission limits and to desk-reject any excess papers by simple ID order. While this policy helps reduce reviewer workload, it may unintentionally discard valuable papers and penalize authors' efforts. In this paper, we ask an essential research question on whether it is possible to follow submission limits while minimizing needless rejections. We first formalize the current desk-rejection policies as an optimization problem, and then develop a practical algorithm based on linear programming relaxation and a rounding scheme. Under extensive evaluation on 11 years of real-world ICLR (International Conference on Learning Representations) data, our method preserves up to $19.23\%$ more papers without violating any author limits. Moreover, our algorithm is highly efficient in practice, with all results on ICLR data computed within at most 53.64 seconds. Our work provides a simple and practical desk-rejection strategy that significantly reduces unnecessary rejections, demonstrating strong potential to improve current CS conference submission policies.
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