4 citations · 8 across the 6 of their papers we have counts for
6 papers
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…
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…
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Chuanhao Li, Hongning Wang
Linear contextual bandit is a popular online learning problem. It has been mostly studied in centralized learning settings. With the surging demand of large-scale decentralized mod…
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Chuanhao Li, Qingyun Wu, Hongning Wang
Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attrac…
Incentivizing Exploration in Linear Bandits under Information Gap
Huazheng Wang, Haifeng Xu, Chuanhao Li +2
We study the problem of incentivizing exploration for myopic users in linear bandits, where the users tend to exploit arm with the highest predicted reward instead of exploring. In…
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though…