​​Economics and Machine Learning​

[Matching markets, Mechanism design, Incentive Theory]

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

 

Mechanism design

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• Online auction design using distribution-free uncertainty quantification with applications to e-commerce. [pdf][code][older version, code]

Jiale Han and Xiaowu Dai*.   

Under R&R at Journal of the American Statistical Association (JASA), 2025.

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• Learn then decide: A learning approach for designing data marketplaces. [pdf​][code]

Yingqi Gao, Jin Zhou, Hua Zhou, Yong Chen, and Xiaowu Dai*.

Preprint, 2025.

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Incentive Theory

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• Incentive-aware recommender systems in two-sided markets. [journal][reprint][preprint][code]

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

ACM Transactions on Recommender Systems (TORS), 2024.

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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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Matching markets​​

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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. 

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• Learning in multi-stage decentralized matching markets. [proceedings][preprint]

Xiaowu Dai and Michael Jordan. 

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

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

Ali Kaazempur-Mofrad, and Xiaowu Dai*.

Under R&R at Annals of Applied Statistics (AoAS), 2024.

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

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