# Matching Papers and Reviewers at Large Conferences

arXiv: https://arxiv.org/abs/2202.12273 · HTML: https://mlsanity.com/paper/2202.12273/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments](https://mlsanity.com/paper/2006.16437/) (2020)
- [PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review](https://mlsanity.com/paper/1806.06237/) (2018)
- [Paper Matching with Local Fairness Constraints](https://mlsanity.com/paper/1905.11924/) (2019)
- [Simple and Effective Paraphrastic Similarity from Parallel Translations](https://mlsanity.com/paper/1909.13872/) (2019)
- [PaRe: A Paper-Reviewer Matching Approach Using a Common Topic Space](https://mlsanity.com/paper/1909.11258/) (2019)
- [Making Paper Reviewing Robust to Bid Manipulation Attacks](https://mlsanity.com/paper/2102.06020/) (2021)
- [Loss Functions, Axioms, and Peer Review](https://mlsanity.com/paper/1808.09057/) (2018)
- [The Conference Paper Assignment Problem: Using Order Weighted Averages to Assign Indivisible Goods](https://mlsanity.com/paper/1705.06840/) (2017)
- [Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design](https://mlsanity.com/paper/2108.06371/) (2021)

## Built on by (most cited first, 18 of 18)

- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025)
- [A Gold Standard Dataset for the Reviewer Assignment Problem](https://mlsanity.com/paper/2303.16750/) (2023)
- [A Dataset on Malicious Paper Bidding in Peer Review](https://mlsanity.com/paper/2207.02303/) (2022)
- [Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies](https://mlsanity.com/paper/2502.00690/) (2025)
- [No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning](https://mlsanity.com/paper/2303.14443/) (2023)
- [On the Detection of Reviewer-Author Collusion Rings From Paper Bidding](https://mlsanity.com/paper/2402.07860/) (2024)
- [Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions](https://mlsanity.com/paper/2412.06606/) (2024)
- [Isotonic Mechanism for Exponential Family Estimation in Machine Learning Peer Review](https://mlsanity.com/paper/2304.11160/) (2023)
- [Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment](https://mlsanity.com/paper/2207.11315/) (2022)
- [RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems](https://mlsanity.com/paper/2601.19637/) (2026)
- [When AI reviews science: Can we trust the referee?](https://mlsanity.com/paper/2604.23593/) (2026)
- [Fair and Welfare-Efficient Constrained Multi-matchings under Uncertainty](https://mlsanity.com/paper/2411.02654/) (2024)
- [Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing](https://mlsanity.com/paper/2411.11437/) (2024)
- [Accept More, Reject Less: Reducing up to 19% Unnecessary Desk-Rejections over 11 Years of ICLR Data](https://mlsanity.com/paper/2506.20141/) (2025)
- [Recommending Best Paper Awards for ML/AI Conferences via the Isotonic Mechanism](https://mlsanity.com/paper/2601.15249/) (2026)
- [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)
- [Allocate Marginal Reviews to Borderline Papers Using LLM Comparative Ranking](https://mlsanity.com/paper/2602.06078/) (2026)

## Abstract (arXiv)

Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper studies a novel reviewer-paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022, and IJCAI 2022. This approach has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer-paper scores; (2) formulating and solving an optimization problem to find good reviewer-paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data -- including a comparison with the matching algorithm used in AAAI's previous (2020) iteration -- and supplements this with additional numerical experimentation.