The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Open accessAbstract
Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and general resource allocation.Traditionally, the primary goal of such models is to maximize a linear function of the constituent matches (e.g., linear social welfare maximization) subject to some constraints.Recent work has studied a new goal of balancing whole-match diversity and economic efficiency, where the objective is instead a monotone submodular function over the matching.Basic versions of this problem are solvable in polynomial time.In this work, we prove that the problem of simultaneously maximizing diversity along several features (e.g., country of citizenship, gender, skills) is NP-hard.To address this problem, we develop the first combinatorial algorithm that constructs provably-optimal diverse b-matchings in pseudo-polynomial time.We also provide a Mixed-Integer Quadratic formulation for the same problem and show that our method guarantees optimal solutions and takes less computation time for a reviewer assignment application.The source code is made available at https://github.com/ faezahmed/diverse matching.
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Does a different view create something new? The effect of employee diversity on innovation
Østergaard, C. R., Timmermans, B., & Kristinsson, K. (2011) · Research Policy · doi:10.1016/j.respol.2010.11.004 · Cited by 1,016
Determinantal Point Processes for Machine Learning
Kulesza, A. (2012) · now publishers, Inc. eBooks · doi:10.1561/9781601986290 · Cited by 493
Grgić-Hlača, N., Zafar, M. B., Gummadi, K. P., & Weller, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11296 · Cited by 181
Shortest paths algorithms: theory and experimental evaluation
Cherkassky, B. V., Goldberg, A. V., & Radzik, T. (1994) · doi:10.5555/314464.314638 · Cited by 153
Learning Mixtures of Submodular Shells with Application to Document SummarizationPreprint
Lin, H. Z., & Bilmes, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1210.4871 · Cited by 101
The Toronto Paper Matching System: An automated paper-reviewer assignment system
Charlin, L., & Zemel, R. S. (2013) · Cited by 99
A robust model for paper reviewer assignment
Liu, X., Suel, T., & Memon, N. D. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645749 · Cited by 75
Constrained multi-aspect expertise matching for committee review assignment
Karimzadehgan, M., & Zhai, C. (2009) · doi:10.1145/1645953.1646207 · Cited by 69
FutureMatch: Combining Human Value Judgments and Machine Learning to Match in Dynamic Environments
Dickerson, J. P., & Sandholm, T. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9239 · Cited by 64
Diverse Weighted Bipartite b-Matching
Ahmed, F., Dickerson, J. P., & Fuge, M. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/6 · Cited by 62
Cited by
Buçinca, Z., Malaya, M. B., & Gajos, K. Z. (2021) · arXiv · doi:10.48550/arxiv.2102.09692 · Cited by 1,124
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757
Notions of explainability and evaluation approaches for explainable artificial intelligence
Vilone, G., & Longo, L. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.009 · Cited by 598
Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study
Hämäläinen, P., Tavast, M., & Kunnari, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580688 · Cited by 248
Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Kim, S. S. Y., Watkins, E. A., Russakovsky, O., Fong, R., & Monroy-Hernández, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581001 · Cited by 233
Metadata
- Published
- 2020-07
- OpenAlex
- W4206908526
- Cited by
- 412
- References
- 14
- Type
- paratext
- Language
- en
- Open access
- gold
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