# Accept More, Reject Less: Reducing up to 19% Unnecessary Desk-Rejections over 11 Years of ICLR Data

arXiv: https://arxiv.org/abs/2506.20141 · HTML: https://mlsanity.com/paper/2506.20141/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 81 references that are in the map.
- First posted on arXiv 25 Jun 2025.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Video Diffusion Models](https://mlsanity.com/paper/2204.03458/) (2022)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [Solving Linear Programs in the Current Matrix Multiplication Time](https://mlsanity.com/paper/1810.07896/) (2018)
- [An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications](https://mlsanity.com/paper/2004.04250/) (2020)
- [Solving Empirical Risk Minimization in the Current Matrix Multiplication Time](https://mlsanity.com/paper/1905.04447/) (2019)
- [A Faster Interior Point Method for Semidefinite Programming](https://mlsanity.com/paper/2009.10217/) (2020)
- [Faster Dynamic Matrix Inverse for Faster LPs](https://mlsanity.com/paper/2004.07470/) (2020)
- [A Deterministic Linear Program Solver in Current Matrix Multiplication Time](https://mlsanity.com/paper/1910.11957/) (2019)
- [A Faster Small Treewidth SDP Solver](https://mlsanity.com/paper/2211.06033/) (2022)
- [Solving SDP Faster: A Robust IPM Framework and Efficient Implementation](https://mlsanity.com/paper/2101.08208/) (2021)
- [A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time](https://mlsanity.com/paper/2309.07418/) (2023)
- [Speeding Up Sparsification using Inner Product Search Data Structures](https://mlsanity.com/paper/2204.03209/) (2022)
- [Solving Tall Dense Linear Programs in Nearly Linear Time](https://mlsanity.com/paper/2002.02304/) (2020)
- [The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?](https://mlsanity.com/paper/2401.02452/) (2024)
- [Matching Papers and Reviewers at Large Conferences](https://mlsanity.com/paper/2202.12273/) (2022)
- [Solving Linear Programs with Sqrt(rank) Linear System Solves](https://mlsanity.com/paper/1910.08033/) (2019)
- [Rounding Sum-of-Squares Relaxations](https://mlsanity.com/paper/1312.6652/) (2013)
- [Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies](https://mlsanity.com/paper/2502.00690/) (2025)
- [Streaming Semidefinite Programs: O(√n) Passes, Small Space and Fast Runtime](https://mlsanity.com/paper/2309.05135/) (2023)
- [Group Fairness in Peer Review](https://mlsanity.com/paper/2410.03474/) (2024)
- [Path Finding I :Solving Linear Programs with ~O(sqrt(rank)) Linear System Solves](https://mlsanity.com/paper/1312.6677/) (2013)
- [On Augmentation Algorithms for Linear and Integer-Linear Programming: From Edmonds-Karp to Bland and Beyond](https://mlsanity.com/paper/1408.3518/) (2014)

## Built on by (most cited first, 1 of 1)

- [Which Coauthor Should I Nominate in My 99 ICLR Submissions? A Mathematical Analysis of the ICLR 2026 Reciprocal Reviewer Nomination Policy](https://mlsanity.com/paper/2512.17950/) (2025)

## Abstract (arXiv)

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.