Chuanhao Li

Orcid: 0009-0006-2440-0596

Affiliations:

Tsinghua University, Department of Industrial Engineering, Beijing, China
Yale University, Department of Statistics and Data Science, New Haven, CT, USA (former)
University of Virginia, Department of Computer Science, Charlottesville, VA, USA (PhD)
Harbin Institute of Technology, State Key Laboratory of Robotics and System, Harbin, China (former)

According to our database¹, Chuanhao Li authored at least 30 papers between 2017 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Bibliography

2026

Training Language Models for Bilateral Trade with Private Information.

[BibT_eX]

[DOI]

CoRR, April, 2026

Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism.

[BibT_eX]

[DOI]

Zheyu Chen

Zhuohuan Li

Chuanhao Li

CoRR, March, 2026

2025

Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation.

[BibT_eX]

[DOI]

CoRR, October, 2025

Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Provably Efficient Algorithm for Best Scoring Rule Identification in Online Principal-Agent Information Acquisition.

[BibT_eX]

[DOI]

Zichen Wang

Chuanhao Li

Huazheng Wang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making.

[BibT_eX]

[DOI]

CoRR, 2024

User Welfare Optimization in Recommender Systems with Competing Content Creators.

[BibT_eX]

[DOI]

CoRR, 2024

Pure Exploration in Asynchronous Federated Bandits.

[BibT_eX]

[DOI]

Proceedings of the Uncertainty in Artificial Intelligence, 2024

PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

User Welfare Optimization in Recommender Systems with Competing Content Creators.

[BibT_eX]

[DOI]

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Human vs. Generative AI in Content Creation Competition: Symbiosis or Conflict?

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Incentivized Truthful Communication for Federated Bandits.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Communication-Efficient Federated Non-Linear Bandit Optimization.

[BibT_eX]

[DOI]

Chuanhao Li

Chong Liu

Yu-Xiang Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Federated Linear Contextual Bandits with Heterogeneous Clients.

[BibT_eX]

[DOI]

Ethan Blaser

Chuanhao Li

Hongning Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Incentivizing Exploration in Linear Contextual Bandits under Information Gap.

[BibT_eX]

[DOI]

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?

[BibT_eX]

[DOI]

Fan Yao

Chuanhao Li

Karthik Abinav Sankararaman

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Incentivized Communication for Federated Bandits.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

How Bad is Top-K Recommendation under Competing Content Creators?

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Communication Efficient Distributed Learning for Kernelized Contextual Bandits.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Communication Efficient Federated Learning for Generalized Linear Bandits.

[BibT_eX]

[DOI]

Chuanhao Li

Hongning Wang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning from a Learning User for Optimal Recommendations.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits.

[BibT_eX]

[DOI]

Chuanhao Li

Hongning Wang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Learning the Optimal Recommendation from Explorative Users.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Incentivizing Exploration in Linear Bandits under Information Gap.

[BibT_eX]

[DOI]

CoRR, 2021

When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution.

[BibT_eX]

[DOI]

Chuanhao Li

Qingyun Wu

Hongning Wang

Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

Unifying Clustered and Non-stationary Bandits.

[BibT_eX]

[DOI]

Chuanhao Li

Qingyun Wu

Hongning Wang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2018

ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis.

[BibT_eX]

[DOI]

Neurocomputing, 2018

Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder.

[BibT_eX]

[DOI]

IEEE Access, 2018

2017

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals.

[BibT_eX]

[DOI]

Sensors, 2017

Chuanhao Li

Timeline

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Bibliography

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