top of page

Economics and Machine Learning

[Matching markets, Mechanism design, Theory of incentives]

(Note:     student author;  * corresponding author.)

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

Xiaowu Dai and Michael Jordan. 

Journal of Machine Learning Research (JMLR), 2021.

Xiaowu Dai and Michael Jordan. 

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

  • 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 (JRSSB), 2023.

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

Xiaowu Dai* and Hengzhi He.

Preprint, 2023+; Under Revisions at Journal of the American Statistical Association: Theory and Methods.

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

Yuantong Li, Guang Cheng, and Xiaowu Dai*. 

International Conference on Machine Learning (ICML), 2024.

  • Incentive-aware recommender systems in two-sided markets. [pdf][code]

Xiaowu Dai*, Yuan Qi, and Michael Jordan.

Preprint, 2023+; Under Revisions at ACM Transactions on Recommender Systems.

Jiale Han and Xiaowu Dai*. 

Preprint, 2024+.

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

bottom of page