6 citations · 28 across the 20 of their papers we have counts for
23 papers
Markov Persuasion Processes with Endogenous Agent Beliefs
Krishnamurthy Iyer, Haifeng Xu, You Zu
We consider a dynamic Bayesian persuasion setting where a single long-lived sender persuades a stream of ``short-lived'' agents (receivers) by sharing information about a payoff-re…
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
Fan Yao, Chuanhao Li, Karthik Abinav Sankararaman +5
The past decade has witnessed the flourishing of a new profession as media content creators, who rely on revenue streams from online content recommendation platforms. The reward me…
Bayesian Calibrated Click-Through Auction
Junjie Chen, Minming Li, Haifeng Xu +1
We study information design in click-through auctions, in which the bidders/advertisers bid for winning an opportunity to show their ads but only pay for realized clicks. The payme…
Robust Stackelberg Equilibria
Jiarui Gan, Minbiao Han, Jibang Wu +1
This paper provides a systematic study of the robust Stackelberg equilibrium (RSE), which naturally generalizes the widely adopted solution concept of the strong Stackelberg equili…
How Bad is Top- Recommendation under Competing Content Creators?
Fan Yao, Chuanhao Li, Denis Nekipelov +2
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the content distribution. However, how the creators' co…
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Stephanie Schoch, Haifeng Xu, Yangfeng Ji
Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label det…
Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality
Jibang Wu, Weiran Shen, Fei Fang +1
Optimizing strategic decisions (a.k.a. computing equilibrium) is key to the success of many non-cooperative multi-agent applications. However, in many real-world situations, we may…
Information Design in the Principal-Agent Problem
Yakov Babichenko, Inbal Talgam-Cohen, Haifeng Xu +1
We study a variant of the principal-agent problem in which the principal does not directly observe the agent's effort outcome; rather, she gets a signal about the agent's action ac…
Generalized Principal-Agency: Contracts, Information, Games and Beyond
Jiarui Gan, Minbiao Han, Jibang Wu +1
In the principal-agent problem formulated by Myerson'82, agents have private information (type) and make private decisions (action), both of which are unobservable to the principal…
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards
Ashwinkumar Badanidiyuru, Zhe Feng, Tianxi Li +1
Incrementality, which is used to measure the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advert…
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…
No-Regret Bayesian Recommendation to Homogeneous Users
Yiding Feng, Wei Tang, Haifeng Xu
We introduce and study the online Bayesian recommendation problem for a recommender system platform. The platform has the privilege to privately observe a utility-relevant \emph{st…
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…