[19] Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. Recommendations as treatments: Debiasing learning and evaluation. In International Conference on Machine Learning, pages 1670-1679. PMLR, 2016.
[20] Alexandre Gilotte, Clément Calauzènes, Thomas Nedelec, Alexandre Abraham, and Simon Dolle. Offline a/b testing for recommender systems. In ACM International Conference on Web Search and Data Mining, pages 198-206, 2018.
[21] Léon Bottou, Jonas Peters, Joaquin Quinonero-Candela, Denis X Charles, D Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, and Ed Snelson. Counterfactual reasoning and learning systems: The example of computational advertising. Journal of Machine Learning Research, 14(11), 2013.
[22] Alex Wood-Doughty and Cameron Bruggeman. The incentives platform at lyft. In ACM International Conference on Web Search and Data Mining, pages 1654-1654, 2022.
[23] David Mimno and Andrew McCallum. Expertise modeling for matching papers with reviewers. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 500-509. ACM, 2007.
[24] Xiang Liu, Torsten Suel, and Nasir Memon. A robust model for paper reviewer assignment. In ACM Conference on Recommender Systems, pages 25-32, 2014.
[25] Marko A. Rodriguez and Johan Bollen. An algorithm to determine peer-reviewers. In ACM Conference on Information and Knowledge Management, pages 319-328. ACM, 2008.
[26] Hong Diep Tran, Guillaume Cabanac, and Gilles Hubert. Expert suggestion for conference program committees. In International Conference on Research Challenges in Information Science, pages 221-232, May 2017.
[27] Laurent Charlin and Richard S. Zemel. The Toronto Paper Matching System: An automated paper-reviewer assignment system. In ICML Workshop on Peer Reviewing and Publishing Models, 2013.
[28] Graham Neubig, John Wieting, Arya McCarthy, Amanda Stent, Natalie Schluter, and Trevor Cohn. Acl reviewer matching code. https://github.com/acl-org/reviewer-paper-matching, 2020. Accessed May 17, 2023.
[29] Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, and Ulrike Von Luxburg. Design and analysis of the nips 2016 review process. Journal of Machine Learning Research, 2018.
[30] Cheng Long, Raymond Wong, Yu Peng, and Liangliang Ye. On good and fair paper-reviewer assignment. In IEEE International Conference on Data Mining, pages 1145-1150, 12 2013.
[31] Judy Goldsmith and Robert H. Sloan. The AI conference paper assignment problem. AAAI Workshop, WS-07-10:53-57, 12 2007.
[32] Wenbin Tang, Jie Tang, and Chenhao Tan. Expertise matching via constraint-based optimization. In International Conference on Web Intelligence and Intelligent Agent Technology, pages 34-41. IEEE Computer Society, 2010.
[33] Peter A. Flach, Sebastian Spiegler, Bruno Golénia, Simon Price, John Guiver, Ralf Herbrich, Thore Graepel, and Mohammed J. Zaki. Novel tools to streamline the conference review process: Experiences from SIGKDD'09. SIGKDD Explorations Newsletter, 11(2):63-67, May 2010.
[34] Camillo J. Taylor. On the optimal assignment of conference papers to reviewers. Technical report, Department of Computer and Information Science, University of Pennsylvania, 2008.
[35] Ivan Stelmakh, Nihar B. Shah, and Aarti Singh. PeerReview4All: Fair and accurate reviewer assignment in peer review. In Algorithmic Learning Theory, 2019.
[36] Ari Kobren, Barna Saha, and Andrew McCallum. Paper matching with local fairness constraints. In ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 1247-1257, 2019.
[37] Komal Dhull, Steven Jecmen, Pravesh Kothari, and Nihar B. Shah. The price of strategyproofing peer assessment. In AAAI Conference on Human Computation and Crowdsourcing, 2022.
[38] Ivan Stelmakh, Nihar B. Shah, Aarti Singh, and Hal Daume III. A novice-reviewer experiment to address scarcity of qualified reviewers in large conferences. In AAAI Conference on Artificial Intelligence, volume 35, pages 4785-4793, 2021.