6 citations · 15 across the 7 of their papers we have counts for
6 papers
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
Jibang Wu, Zixuan Zhang, Zhe Feng +4
In today's economy, it becomes important for Internet platforms to consider the sequential information design problem to align its long term interest with incentives of the gig ser…
Learning from a Learning User for Optimal Recommendations
Fan Yao, Chuanhao Li, Denis Nekipelov +2
In real-world recommendation problems, especially those with a formidably large item space, users have to gradually learn to estimate the utility of any fresh recommendations from…
(Almost) Free Incentivized Exploration from Decentralized Learning Agents
Chengshuai Shi, Haifeng Xu, Wei Xiong +1
Incentivized exploration in multi-armed bandits (MAB) has witnessed increasing interests and many progresses in recent years, where a principal offers bonuses to agents to do explo…
Least Square Calibration for Peer Review
Sijun Tan, Jibang Wu, Xiaohui Bei +1
Peer review systems such as conference paper review often suffer from the issue of miscalibration. Previous works on peer review calibration usually only use the ordinal informatio…
Learning the Optimal Recommendation from Explorative Users
Fan Yao, Chuanhao Li, Denis Nekipelov +2
We propose a new problem setting to study the sequential interactions between a recommender system and a user. Instead of assuming the user is omniscient, static, and explicit, as…
The Limits of Optimal Pricing in the Dark
Quinlan Dawkins, Minbiao Han, Haifeng Xu
A ubiquitous learning problem in today's digital market is, during repeated interactions between a seller and a buyer, how a seller can gradually learn optimal pricing decisions ba…