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ORCIDJinglin Chen
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2020 – today
- 2024
[j2]Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
Model-Free Representation Learning and Exploration in Low-Rank MDPs. J. Mach. Learn. Res. 25: 6:1-6:76 (2024)- 2023
- [c10]Audrey Huang, Jinglin Chen, Nan Jiang:
Extended Abstract: Learning in Low-rank MDPs with Density Features. CISS 2023: 1-3 - [c9]Audrey Huang, Jinglin Chen, Nan Jiang:
Reinforcement Learning in Low-rank MDPs with Density Features. ICML 2023: 13710-13752 - [i10]Audrey Huang, Jinglin Chen, Nan Jiang:
Reinforcement Learning in Low-Rank MDPs with Density Features. CoRR abs/2302.02252 (2023) - 2022
- [j1]Huozhi Zhou, Jinglin Chen, Lav R. Varshney, Ashish Jagmohan:
Nonstationary Reinforcement Learning with Linear Function Approximation. Trans. Mach. Learn. Res. 2022 (2022) - [c8]Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu:
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. ICLR 2022 - [c7]Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS 2022 - [c6]Jinglin Chen, Nan Jiang:
Offline reinforcement learning under value and density-ratio realizability: The power of gaps. UAI 2022: 378-388 - [i9]Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu:
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. CoRR abs/2202.06450 (2022) - [i8]Jinglin Chen, Nan Jiang:
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps. CoRR abs/2203.13935 (2022) - [i7]Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. CoRR abs/2206.10770 (2022) - 2021
- [c5]Priyank Agrawal, Jinglin Chen, Nan Jiang:
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. AAAI 2021: 6566-6573 - [i6]Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal:
Model-free Representation Learning and Exploration in Low-rank MDPs. CoRR abs/2102.07035 (2021) - 2020
- [i5]Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang:
Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion. CoRR abs/2007.01990 (2020) - [i4]Huozhi Zhou, Jinglin Chen, Lav R. Varshney, Ashish Jagmohan:
Nonstationary Reinforcement Learning with Linear Function Approximation. CoRR abs/2010.04244 (2020) - [i3]Priyank Agrawal, Jinglin Chen, Nan Jiang:
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. CoRR abs/2010.12163 (2020)
2010 – 2019
- 2019
- [c4]Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang:
Accelerating Nonconvex Learning via Replica Exchange Langevin diffusion. ICLR (Poster) 2019 - [c3]Jinglin Chen, Nan Jiang:
Information-Theoretic Considerations in Batch Reinforcement Learning. ICML 2019: 1042-1051 - [i2]Jinglin Chen, Nan Jiang:
Information-Theoretic Considerations in Batch Reinforcement Learning. CoRR abs/1905.00360 (2019) - 2018
- [c2]Jinglin Chen, Jian Peng, Qiang Liu:
Efficient Localized Inference for Large Graphical Models. IJCAI 2018: 4987-4993 - 2017
- [i1]Jinglin Chen, Jian Peng, Qiang Liu:
Efficient Localized Inference for Large Graphical Models. CoRR abs/1710.10404 (2017)
2000 – 2009
- 2008
- [c1]Zhiwen Bai, Liming Wang, Jinglin Chen, Lin Xu, Jian Liu, Xiyang Liu:
DTAD: A Dynamic Taint Analysis Detector for Information Security. WAIM 2008: 591-597
Coauthor Index
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