research
∙
09/21/2023
Incentivized Communication for Federated Bandits
Most existing works on federated bandits take it for granted that all cl...
share
research
∙
06/13/2023
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
The past decade has witnessed the flourishing of a new profession as med...
research
∙
02/03/2023
How Bad is Top-K Recommendation under Competing Content Creators?
Content creators compete for exposure on recommendation platforms, and s...
research
∙
06/10/2022
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
We tackle the communication efficiency challenge of learning kernelized ...
research
∙
02/03/2022
Learning from a Learning User for Optimal Recommendations
In real-world recommendation problems, especially those with a formidabl...
research
∙
02/02/2022
Communication Efficient Federated Learning for Generalized Linear Bandits
Contextual bandit algorithms have been recently studied under the federa...
research
∙
10/06/2021
Learning the Optimal Recommendation from Explorative Users
We propose a new problem setting to study the sequential interactions be...
research
∙
10/04/2021
Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
Linear contextual bandit is a popular online learning problem. It has be...
research
∙
04/14/2021
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Collaborative bandit learning, i.e., bandit algorithms that utilize coll...
research
∙
04/08/2021
Incentivizing Exploration in Linear Bandits under Information Gap
We study the problem of incentivizing exploration for myopic users in li...
research
∙
09/05/2020
