no code implementations • 15 Mar 2023 • Siyu Chen, Jibang Wu, Yifan Wu, Zhuoran Yang
Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information.
no code implementations • 22 Feb 2022 • Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu
This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs), where a sender, with informational advantage, seeks to persuade a stream of myopic receivers to take actions that maximizes the sender's cumulative utilities in a finite horizon Markovian environment with varying prior and utility functions.
1 code implementation • NeurIPS 2021 • Sijun Tan, Jibang Wu, Xiaohui Bei, Haifeng Xu
Peer review systems such as conference paper review often suffer from the issue of miscalibration.
no code implementations • 10 Nov 2021 • Jibang Wu, Haifeng Xu, Fan Yao
Under the uncoupled learning setup, the last-iterate convergence guarantee towards Nash equilibrium is shown to be impossible in many games.
1 code implementation • 25 Oct 2021 • Sijun Tan, Jibang Wu, Xiaohui Bei, Haifeng Xu
Peer review systems such as conference paper review often suffer from the issue of miscalibration.
no code implementations • 29 Jan 2020 • Jibang Wu, Renqin Cai, Hongning Wang
Predicting users' preferences based on their sequential behaviors in history is challenging and crucial for modern recommender systems.