publications

2025

1. ICML

   The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability

   Jiachen Hu, Rui Ai, Han Zhong, and 4 more authors

   In Proceedings of the 42nd International Conference on Machine Learning, 2025

   arXiv Bib HTML

   @inproceedings{hu2025strategic,
     title = {The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability},
     author = {Hu, Jiachen and Ai, Rui and Zhong, Han and Chen, Xiaoyu and Wang, Liwei and Wang, Zhaoran and Yang, Zhuoran},
     booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
     year = {2025},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
   }

2. Preprint

   New Sphere Packings from the Antipode Construction

   Ruitao Chen, Jiachen Hu, Binghui Li, and 2 more authors

   In arXiv preprint, 2025

   arXiv Bib HTML

   We construct non-lattice sphere packings in dimensions 19, 20, 21, 23, 44, 45, and 47, demonstrating record densities that surpass all previously documented results in these dimensions. The construction applies the antipode method to suboptimal cross-sections of \(\Lambda_{24}\) and \(P_{48p}\).

   @inproceedings{chen2025spherepacking,
     title = {New Sphere Packings from the Antipode Construction},
     author = {Chen, Ruitao and Hu, Jiachen and Li, Binghui and Wang, Liwei and Wu, Tianyi},
     booktitle = {arXiv preprint},
     year = {2025},
   }

2024

1. Preprint

   On Limitation of Transformer for Learning HMMs

   Jiachen Hu, Qinghua Liu, and Chi Jin

   In arXiv preprint, 2024

   arXiv Bib HTML

   @inproceedings{hu2024limitation,
     title = {On Limitation of Transformer for Learning {HMMs}},
     author = {Hu, Jiachen and Liu, Qinghua and Jin, Chi},
     booktitle = {arXiv preprint},
     year = {2024},
   }

2. ICML

   Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

   Han Zhong^*, Jiachen Hu^*, Yecheng Xue, and 2 more authors

   In Proceedings of the 41st International Conference on Machine Learning, 2024

   arXiv Bib HTML

   @inproceedings{pmlr-v235-zhong24b,
     title = {Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret},
     author = {Zhong, Han and Hu, Jiachen and Xue, Yecheng and Li, Tongyang and Wang, Liwei},
     booktitle = {Proceedings of the 41st International Conference on Machine Learning},
     pages = {61681--61707},
     year = {2024},
     volume = {235},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
   }

3. TQC

   Quantum Non-Identical Mean Estimation: Efficient Algorithms and Fundamental Limits

   Jiachen Hu, Tongyang Li, Xinzhao Wang, and 3 more authors

   In 19th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2024), 2024

   DOI arXiv Bib HTML

   @inproceedings{hu_et_al:LIPIcs.TQC.2024.9,
     title = {Quantum Non-Identical Mean Estimation: Efficient Algorithms and Fundamental Limits},
     author = {Hu, Jiachen and Li, Tongyang and Wang, Xinzhao and Xue, Yecheng and Zhang, Chenyi and Zhong, Han},
     booktitle = {19th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2024)},
     pages = {9:1--9:21},
     year = {2024},
     volume = {310},
     series = {Leibniz International Proceedings in Informatics (LIPIcs)},
     publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
     doi = {10.4230/LIPIcs.TQC.2024.9},
   }

4. FC

   ZeroSwap: Data-Driven Optimal Market Making in Decentralized Finance

   Viraj Nadkarni, Jiachen Hu, Ranvir Rana, and 3 more authors

   In Financial Cryptography and Data Security, 2024

   arXiv Bib HTML

   @inproceedings{nadkarni2024zeroswap,
     title = {{ZeroSwap}: Data-Driven Optimal Market Making in Decentralized Finance},
     author = {Nadkarni, Viraj and Hu, Jiachen and Rana, Ranvir and Jin, Chi and Kulkarni, Sanjeev and Viswanath, Pramod},
     booktitle = {Financial Cryptography and Data Security},
     pages = {209--227},
     year = {2024},
   }

2023

1. ICLR

   Provable Sim-to-real Transfer in Continuous Domain with Partial Observations

   Jiachen Hu^*, Han Zhong^*, Chi Jin, and 1 more author

   In International Conference on Learning Representations, 2023

   arXiv Bib HTML

   

   We study sim-to-real transfer in continuous domains with partial observations, modeled by linear quadratic Gaussian (LQG) systems. We show that a popular robust adversarial training algorithm can learn a policy from simulation that is competitive to the optimal real-world policy, providing the first provable guarantee in this setting.

   @inproceedings{hu2023provable,
     title = {Provable Sim-to-real Transfer in Continuous Domain with Partial Observations},
     author = {Hu, Jiachen and Zhong, Han and Jin, Chi and Wang, Liwei},
     booktitle = {International Conference on Learning Representations},
     year = {2023},
   }

2022

1. ICLR

   Understanding Domain Randomization for Sim-to-real Transfer

   Xiaoyu Chen^*, Jiachen Hu^*, Chi Jin, and 2 more authors

   In International Conference on Learning Representations(Spotlight, top 6%) , 2022

   arXiv Bib HTML

   

   We provide a theoretical framework for domain randomization, modeling the simulator as a set of MDPs with tunable parameters. We prove sharp bounds on the sim-to-real gap and show that successful transfer is achievable without any real-world training samples, highlighting the importance of history-dependent policies.

   @inproceedings{chen2022understanding,
     title = {Understanding Domain Randomization for Sim-to-real Transfer},
     author = {Chen, Xiaoyu and Hu, Jiachen and Jin, Chi and Li, Lihong and Wang, Liwei},
     booktitle = {International Conference on Learning Representations},
     year = {2022},
   }

2. ICLR

   Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver

   Xiaoyu Chen, Jiachen Hu, Lin F. Yang, and 1 more author

   In International Conference on Learning Representations(Spotlight, top 6%) , 2022

   arXiv Bib HTML

   @inproceedings{chen2022nearoptimal,
     title = {Near-Optimal Reward-Free Exploration for Linear Mixture {MDPs} with Plug-in Solver},
     author = {Chen, Xiaoyu and Hu, Jiachen and Yang, Lin F. and Wang, Liwei},
     booktitle = {International Conference on Learning Representations},
     year = {2022},
   }

2021

1. ICML

   Near-Optimal Representation Learning for Linear Bandits and Linear RL

   Jiachen Hu^*, Xiaoyu Chen^*, Chi Jin, and 2 more authors

   In Proceedings of the 38th International Conference on Machine Learning, 2021

   arXiv Bib HTML

   

   We study multi-task representation learning for linear bandits and episodic RL with linear value function approximation. Our algorithm MTLR-OFUL achieves \(\tilde{O}(M\sqrt{dkT} + d\sqrt{kMT})\) regret, significantly improving over the \(\tilde{O}(Md\sqrt{T})\) baseline, yielding the first theoretical characterization of multi-task representation learning benefits in RL exploration.

   @inproceedings{pmlr-v139-hu21a,
     title = {Near-Optimal Representation Learning for Linear Bandits and Linear {RL}},
     author = {Hu, Jiachen and Chen, Xiaoyu and Jin, Chi and Li, Lihong and Wang, Liwei},
     booktitle = {Proceedings of the 38th International Conference on Machine Learning},
     pages = {4349--4358},
     year = {2021},
     volume = {139},
     series = {Proceedings of Machine Learning Research},
     publisher = {PMLR},
   }

2. ICLR

   Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL

   Xiaoyu Chen, Jiachen Hu, Lihong Li, and 1 more author

   In International Conference on Learning Representations, 2021

   arXiv Bib HTML

   @inproceedings{chen2021efficient,
     title = {Efficient Reinforcement Learning in Factored {MDPs} with Application to Constrained {RL}},
     author = {Chen, Xiaoyu and Hu, Jiachen and Li, Lihong and Wang, Liwei},
     booktitle = {International Conference on Learning Representations},
     year = {2021},
   }

2020

1. ICLR

   Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication

   Yuanhao Wang^*, Jiachen Hu^*, Xiaoyu Chen, and 1 more author

   In International Conference on Learning Representations, 2020

   arXiv Bib HTML

   

   We design communication protocols for distributed bandit learning with M agents under central coordination. For multi-armed bandits, we achieve near-optimal regret with only \(O(M\log(MK))\) communication cost — independent of the time horizon T and matching the lower bound up to a log factor.

   @inproceedings{wang2020distributed,
     title = {Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication},
     author = {Wang, Yuanhao and Hu, Jiachen and Chen, Xiaoyu and Wang, Liwei},
     booktitle = {International Conference on Learning Representations},
     year = {2020},
   }