6 citations · 28 across the 20 of their papers we have counts for
10 papers · 1 filter
Multi-Channel Bayesian Persuasion
Yakov Babichenko, Inbal Talgam-Cohen, Haifeng Xu +1
The celebrated Bayesian persuasion model considers strategic communication between an informed agent (the sender) and uninformed decision makers (the receivers). The current rapidl…
Uncoupled Bandit Learning towards Rationalizability: Benchmarks, Barriers, and Algorithms
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. This work studies the last-iterate conv…
(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…
When Are Linear Stochastic Bandits Attackable?
Huazheng Wang, Haifeng Xu, Hongning Wang
We study adversarial attacks on linear stochastic bandits: by manipulating the rewards, an adversary aims to control the behaviour of the bandit algorithm. Perhaps surprisingly, we…
Algorithmic Information Design in Multi-Player Games: Possibility and Limits in Singleton Congestion
Chenghan Zhou, Thanh H. Nguyen, Haifeng Xu
Most algorithmic studies on multi-agent information design so far have focused on the restricted situation with no inter-agent externalities; a few exceptions investigated truly st…
Auctioning with Strategically Reticent Bidders
Jibang Wu, Ashwinkumar Badanidiyuru, Haifeng Xu
We propose and study a novel mechanism design setup where each bidder holds two kinds of private information: (1) type variable, which can be misreported; (2) information variable,…
Competitive Information Design for Pandora's Box
Bolin Ding, Yiding Feng, Chien-Ju Ho +2
We study a natural competitive-information-design strategic variant for the celebrated Pandora's Box problem (Weitzman, 1979), where each box is associated with a strategic informa…