6 citations · 28 across the 20 of their papers we have counts for
8 papers · 1 filter
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…