I am a PhD student at the Sigma Lab@UChicago, advised by Prof. Haifeng Xu. Prior to UChicago, I received my BA/MS degrees in CS from UVA in 2019, where I worked on recommendation algorithms with Prof. Hongning Wang.
My current research lies at the interface between game theory, learning theory and optimization, with the primary focus on modeling and solving multi-agent decision-making problems under complex, unknown environment. More broadly, I aim to advance the design principles and approaches of intelligent systems towards strategic alignment — a concept centered on aligning the interests of all stakeholders to achieve mutually beneficial outcomes.
Towards this end, I am interested in developing practical techniques to 1) build incentive-aware AI agents with strategic intelligence and rationalizable behaviors; 2) align the economic incentives of users, model developers and data providers for more sustainable AI ecosystems.
@article{wu2024contractual,title={Contractual Reinforcement Learning: Pulling Arms with Invisible Hands},author={Wu, Jibang and Chen, Siyu and Wang, Mengdi and Wang, Huazheng and Xu, Haifeng},journal={arXiv preprint arXiv:2407.01458},year={2024},}
A Truth Serum for Eliciting Self-Evaluations in Scientific Reviews
@article{wu2023isotonic,title={A Truth Serum for Eliciting Self-Evaluations in Scientific Reviews},author={Wu, Jibang and Xu, Haifeng and Guo, Yifan and Su, Weijie},}
Auctioning with Strategically Reticent Bidders
Jibang Wu, Ashwinkumar Badanidiyuru, and Haifeng Xu
@inproceedings{wu2021auctioning,title={Auctioning with Strategically Reticent Bidders},author={Wu, Jibang and Badanidiyuru, Ashwinkumar and Xu, Haifeng},booktitle={Proceedings of the 20th Conference on Web and Internet Economics},year={2024},series={WINE '24},}
Generalized Principal-Agency: Contracts, Information, Games and Beyond
(α-β) Jiarui Gan, Minbiao Han, Jibang Wu, and Haifeng Xu
@inproceedings{gan2022optimal,title={Generalized Principal-Agency: Contracts, Information, Games and Beyond},author={Gan, Jiarui and Han, Minbiao and Wu, Jibang and Xu, Haifeng},booktitle={Proceedings of the 20th Conference on Web and Internet Economics},year={2024},series={WINE '24},}
Robust Stackelberg Equilibria
(α-β) Jiarui Gan, Minbiao Han, Jibang Wu, and Haifeng Xu
@inproceedings{10.1145/3580507.3597680,author={Gan, Jiarui and Han, Minbiao and Wu, Jibang and Xu, Haifeng},title={Robust Stackelberg Equilibria},year={2023},isbn={9798400701047},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3580507.3597680},booktitle={Proceedings of the 24th ACM Conference on Economics and Computation},pages={735},numpages={1},location={London, United Kingdom},series={EC '23},}
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model
@article{chen2023learning,title={Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model},author={Chen, Siyu and Wu, Jibang and Wu, Yifan and Yang, Zhuoran},journal={arXiv preprint arXiv:2303.08613},year={2023},}
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality
@article{wu2022inverse,title={Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality},author={Wu, Jibang and Shen, Weiran and Fang, Fei and Xu, Haifeng},journal={Advances in Neural Information Processing Systems},volume={35},pages={32186--32198},year={2022},}
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, and Haifeng Xu
@inproceedings{10.1145/3490486.3538313,author={Wu, Jibang and Zhang, Zixuan and Feng, Zhe and Wang, Zhaoran and Yang, Zhuoran and Jordan, Michael I. and Xu, Haifeng},title={Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning},year={2022},isbn={9781450391504},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3490486.3538313},booktitle={Proceedings of the 23rd ACM Conference on Economics and Computation},pages={471–472},numpages={2},location={Boulder, CO, USA},series={EC '22},}
Multi-Agent Learning for Iterative Dominance Elimination: Formal Barriers and New Algorithms
@inproceedings{wu2021multi,title={Multi-Agent Learning for Iterative Dominance Elimination: Formal Barriers and New Algorithms},author={Wu, Jibang and Xu, Haifeng and Yao, Fan},booktitle={Proceedings of Thirty Fifth Conference on Learning Theory},pages={543--543},year={2022},editor={Loh, Po-Ling and Raginsky, Maxim},volume={178},series={Proceedings of Machine Learning Research},month={02--05 Jul},publisher={PMLR},}
Least square calibration for peer reviews
Sijun Tan*, Jibang Wu*, Xiaohui Bei, and Haifeng Xu
@article{tan2021least,title={Least square calibration for peer reviews},author={Tan*, Sijun and Wu*, Jibang and Bei, Xiaohui and Xu, Haifeng},journal={Advances in Neural Information Processing Systems},volume={34},pages={27069--27080},year={2021},}
@inproceedings{cai2021category,title={Category-aware collaborative sequential recommendation},author={Cai, Renqin and Wu, Jibang and San, Aidan and Wang, Chong and Wang, Hongning},booktitle={Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval},pages={388--397},year={2021},status={published},}
Déjà vu: A contextualized temporal attention mechanism for sequential recommendation
@inproceedings{wu2020deja,title={D{\'e}j{\`a} vu: A contextualized temporal attention mechanism for sequential recommendation},author={Wu, Jibang and Cai, Renqin and Wang, Hongning},booktitle={Proceedings of The Web Conference 2020},pages={2199--2209},year={2020},}
