More information about my work can be found on [Google Scholar]

Preprints

  • Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?
    Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
    arXiv 2024 [paper]
  • Pure Exploration in Asynchronous Federated Bandits
    Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang
    arXiv 2023 [paper]

Publications

Sequential decision making & algorithmic game theory

  • Incentivized Truthful Communication for Federated Bandits
    Zhepei Wei*, Chuanhao Li*, Haifeng Xu, Hongning Wang
    ICLR 2024
  • Communication-Efficient Federated Non-Linear Bandit Optimization
    Chuanhao Li*, Chong Liu*, Yu-Xiang Wang
    ICLR 2024 [paper]
  • Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
    Fan Yao, Chuanhao Li, Karthik Abinav Sankararaman, Yiming Liao, Yan Zhu, Qifan Wang, Hongning Wang, Haifeng Xu
    NeurIPS 2023 [paper]
  • Incentivized Communication for Federated Bandits
    Zhepei Wei*, Chuanhao Li*, Haifeng Xu, Hongning Wang
    NeurIPS 2023 [paper]
  • Incentivizing Exploration in Linear Bandits under Information Gap
    Huazheng Wang, Haifeng Xu, Chuanhao Li, Zhiyuan Liu, Hongning Wang
    RecSys 2023 [paper]
  • How Bad is Top-K Recommendation under Competing Content Creators?
    Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
    ICML 2023 [paper]
  • Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment
    Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang
    ICLR 2023 [paper]
  • Communication Efficient Distributed Learning for Kernelized Contextual Bandits
    Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang
    NeurIPS 2022 [paper]
  • Communication Efficient Federated Learning for Generalized Linear Bandits
    Chuanhao Li, Hongning Wang
    NeurIPS 2022 [paper] [code]
  • Learning from a Learning User for Optimal Recommendations
    Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
    ICML 2022 [paper]
  • Learning the Optimal Recommendation from Explorative Users
    Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
    AAAI 2022 [paper]
  • Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
    Chuanhao Li, Hongning Wang
    AISTATS 2022 [paper] [code]
  • When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
    Chuanhao Li, Qingyun Wu, Hongning Wang
    SIGIR 2021 [paper] [code]
  • Unifying Clustered and Non-stationary Bandits
    Chuanhao Li, Qingyun Wu, Hongning Wang
    AISTATS 2021 [paper] [code]

Applications of deep Learning in vibration signals

  • A Deep Convolutional Neural Network with New Training Methods for Bearing Fault Diagnosis under Noisy Environment and Different Working Load
    Wei Zhang, Chuanhao Li, Gaoliang Peng, Yuanhang Chen, Zhujun Zhang
    Mechanical Systems and Signal Processing 2018 [paper]
  • ACDIN: Bridging the Gap between Artificial and Real Bearing Damages for Bearing Fault Diagnosis
    Yuanhang Chen, Gaoliang Peng, Chaohao Xie, Wei Zhang, Chuanhao Li, Shaohui Liu
    Neurocomputing 2018 [paper]
  • Bearing Fault Diagnosis Using Fully-connected Winner-take-all Autoencoder
    Chuanhao Li, Wei Zhang, Gaoliang Peng, Shaohui Liu
    IEEE Access 2017 [paper] [code]
  • A New Deep Learning Model for Fault Diagnosis with Good Anti-noise and Domain Adaptation Ability on Raw Vibration Signals
    Wei Zhang, Gaoliang Peng, Chuanhao Li, Yuanhang Chen, Zhujun Zhang
    Sensors 2017 [paper]
  • Rolling Element Bearings Fault Intelligent Diagnosis Based on Convolutional Neural Networks using Raw Sensing Signal
    Wei Zhang, Gaoliang Peng, Chuanhao Li
    IIH-MSP 2016 [paper]
  • Bearings Fault Diagnosis based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input
    Wei Zhang, Gaoliang Peng, Chuanhao Li
    ICMME 2016 [paper]