4 citations · 22 across the 19 of their papers we have counts for
20 papers
Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference
Zichen Wang, Haoyang Hong, Chuanhao Li +3
In multi-armed bandits with network interference (MABNI), the action taken by one node can influence the rewards of others, creating complex interdependence. While existing researc…
Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition
Zichen Wang, Chuanhao Li, Huazheng Wang
We investigate the problem of identifying the optimal scoring rule within the principal-agent framework for online information acquisition problem. We focus on the principal's pers…
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
Kendong Liu, Zhiyu Zhu, Chuanhao Li +3
In this paper, we make the first attempt to align diffusion models for image inpainting with human aesthetic standards via a reinforcement learning framework, significantly improvi…
User Welfare Optimization in Recommender Systems with Competing Content Creators
Fan Yao, Yiming Liao, Mingzhe Wu +6
Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content reco…
Federated Linear Contextual Bandits with Heterogeneous Clients
Ethan Blaser, Chuanhao Li, Hongning Wang
The demand for collaborative and private bandit learning across multiple agents is surging due to the growing quantity of data generated from distributed systems. Federated bandit…
Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
Fan Yao, Chuanhao Li, Denis Nekipelov +2
The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative approaches to produce diverse, high-quality co…
Incentivized Truthful Communication for Federated Bandits
Zhepei Wei, Chuanhao Li, Tianze Ren +2
To enhance the efficiency and practicality of federated bandit learning, recent advances have introduced incentives to motivate communication among clients, where a client particip…
Communication-Efficient Federated Non-Linear Bandit Optimization
Chuanhao Li, Chong Liu, Yu-Xiang Wang
Federated optimization studies the problem of collaborative function optimization among multiple clients (e.g. mobile devices or organizations) under the coordination of a central…
Incentivized Communication for Federated Bandits
Zhepei Wei, Chuanhao Li, Haifeng Xu +1
Most existing works on federated bandits take it for granted that all clients are altruistic about sharing their data with the server for the collective good whenever needed. Despi…
Pure Exploration in Asynchronous Federated Bandits
Zichen Wang, Chuanhao Li, Chenyu Song +3
We study the federated pure exploration problem of multi-armed bandits and linear bandits, where agents cooperatively identify the best arm via communicating with the central s…
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…
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…
Communication Efficient Distributed Learning for Kernelized Contextual Bandits
Chuanhao Li, Huazheng Wang, Mengdi Wang +1
We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting. Despite the recent advances in communication-efficient distribu…
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…
Communication Efficient Federated Learning for Generalized Linear Bandits
Chuanhao Li, Hongning Wang
Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit…
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…