DTAD: A Dynamic Taint Analysis Detector for Information Security. - Zhiwen Bai; Liming Wang; Jinglin Chen; Lin Xu; Jian Liu; Xiyang Liu (WAIM, 2008)

Efficient Localized Inference for Large Graphical Models. - Jinglin Chen; Jian Peng 001; Qiang Liu 001 (IJCAI, 2018)

Accelerating Nonconvex Learning via Replica Exchange Langevin diffusion. - Yi Chen; Jinglin Chen; Jing Dong 007; Jian Peng 001; Zhaoran Wang 001 (ICLR (Poster), 2019)

Information-Theoretic Considerations in Batch Reinforcement Learning. - Jinglin Chen; Nan Jiang 008 (ICML, 2019)

Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. - Priyank Agrawal; Jinglin Chen; Nan Jiang 008 (AAAI, 2021)

Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. - Jiawei Huang; Jinglin Chen; Li Zhao 007; Tao Qin 001; Nan Jiang 008; Tie-Yan Liu (ICLR, 2022)

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. - Jinglin Chen; Aditya Modi 002; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal (NeurIPS, 2022)

Offline reinforcement learning under value and density-ratio realizability: The power of gaps. - Jinglin Chen; Nan Jiang 008 (UAI, 2022)

Nonstationary Reinforcement Learning with Linear Function Approximation. - Huozhi Zhou; Jinglin Chen; Lav R. Varshney; Ashish Jagmohan (Trans. Mach. Learn. Res., 2022)

Extended Abstract: Learning in Low-rank MDPs with Density Features. - Audrey Huang; Jinglin Chen; Nan Jiang 008 (CISS, 2023)

Reinforcement Learning in Low-rank MDPs with Density Features. - Audrey Huang; Jinglin Chen; Nan Jiang 008 (ICML, 2023)

Model-Free Representation Learning and Exploration in Low-Rank MDPs. - Aditya Modi 002; Jinglin Chen; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal (J. Mach. Learn. Res., 2024)