The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Open accessAbstract
This paper considers the capacity expansion problem in two-sided matchings, where the policymaker is allowed to allocate some extra seats as well as the standard seats.In medical residency match, each hospital accepts a limited number of doctors.Such capacity constraints are typically given in advance.However, such exogenous constraints can compromise the welfare of the doctors; some popular hospitals inevitably dismiss some of their favorite doctors.Meanwhile, it is often the case that the hospitals are also benefited to accept a few extra doctors.To tackle the problem, we propose an anytime method that the upper confidence tree searches the space of capacity expansions, each of which has a resident-optimal stable assignment that the deferred acceptance method finds.Constructing a good search tree representation significantly boosts the performance of the proposed method.Our simulation shows that the proposed method identifies an almost optimal capacity expansion with a significantly smaller computational budget than exact methods based on mixed-integer programming.
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Cites
Mastering the game of Go with deep neural networks and tree search
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A Survey of Monte Carlo Tree Search Methods
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Bandit Based Monte-Carlo Planning
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Algorithmics of Matching Under Preferences
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COLLEGE ADMISSIONS AND THE STABILITY OF MARRIAGE
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The College Admissions problem with lower and common quotas
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Cited by
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
A Decomposition Dynamic graph convolutional recurrent network for traffic forecasting
Weng, W., Fan, J., Wu, H., Hu, Y., Tian, H., Zhu, F., & Wu, J. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109670 · Cited by 267
Rethink reporting of evaluation results in AI
Burnell, R., Schellaert, W., Burden, J., Ullman, T. D., Martinez-Plumed, F., Tenenbaum, J. B., Rutar, D., Cheke, L. G., Sohl-Dickstein, J., Mitchell, M., et al. (2023) · Science · doi:10.1126/science.adf6369 · Cited by 104
Natural Is the Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models
Wang, Y., Li, X., Nguyen, T. N., Wang, S., Ni, C., & Ding, L. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3643753 · Cited by 6
Metadata
- Published
- 2022-07
- OpenAlex
- W4285605356
- Cited by
- 316
- References
- 16
- Type
- paratext
- Language
- en
- Open access
- gold
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