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Economics and Machine Learning​

[Matching markets, Mechanism design, Incentive Theory]

(Note:     students under my supervision;  * corresponding author.)

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  • Learning strategies in decentralized matching markets under uncertain preferences. [journal][preprint]

Xiaowu Dai and Michael Jordan. 

Journal of Machine Learning Research (JMLR), 2021. [lead article]

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Xiaowu Dai and Michael Jordan. 

Advances in Neural Information Processing Systems (NeurIPS), 2021.

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  • Discussion: "Estimating means of bounded random variables by betting" by Waudby-Smith and Ramdas. [journal][reprint​][preprint][code]

Jiayi Li, Yuantong Li, and Xiaowu Dai*.

Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2023.

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  • Two-sided competing matching recommendation markets with quota and complementary preferences constraints. [proceedings][preprint][code]

Yuantong Li, Guang Cheng, and Xiaowu Dai*. 

International Conference on Machine Learning (ICML), 2024.

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Xiaowu Dai*, Wenlu Xu, Yuan Qi, and Michael Jordan.

ACM Transactions on Recommender Systems (TORS), 2024.

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Xiaowu Dai*

Journal of the Royal Statistical Society Series B: Statistical Methodology (JRSSB), 2024. [lead article]

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  • A data envelopment analysis approach for assessing fairness in resource allocation: Application to kidney exchange programs. [pdf][code]

Ali Kaazempur-Mofrad, and Xiaowu Dai*.

Preprint, 2024.

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  • An ODE model for dynamic matching in heterogeneous networks. [pdf][code]

Xiaowu Dai* and Hengzhi He.

Preprint, 2023.

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  • Dynamic online recommendation for two-sided market with Bayesian incentive compatibility. [pdf]

Yuantong Li, Guang Cheng, and Xiaowu Dai*.

Preprint, 2024.

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Jiale Han and Xiaowu Dai*. 

Preprint, 2024.

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I'm also interested in designing real-world matching systems. For example, I designed Hilbert Matching [App Store] for the clothing industry. 

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