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Lingxiao Wang 0003
Person information
- affiliation: Northwestern University, Department of Industrial Engineering and Management Sciences, Evanston, IL, USA
Other persons with the same name
- Lingxiao Wang — disambiguation page
- Lingxiao Wang 0001 — Toyota Technological Institute at Chicago, IL, USA (and 2 more)
- Lingxiao Wang 0002 — Grenoble Alpes University, France
- Lingxiao Wang 0004
— Auburn University, Department of Electrical and Computer Engineering, AL, USA
- Lingxiao Wang 0005
- Lingxiao Wang 0006
- Lingxiao Wang 0007
- Lingxiao Wang 0008
- Lingxiao Wang 0009 — Tsinghua University, Department of Electronic Engineering, Beijing, China
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2020 – today
- 2024
- [j5]Chenjia Bai
Pessimistic value iteration for multi-task data sharing in Offline Reinforcement Learning. Artif. Intell. 326: 104048 (2024) - [j4]Zhi-Hong Deng
False Correlation Reduction for Offline Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46(2): 1199-1211 (2024) - [j3]Chenjia Bai
Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 35(7): 8954-8968 (2024) - [i15]Chenjia Bai, Lingxiao Wang, Jianye Hao, Zhuoran Yang, Bin Zhao, Zhen Wang, Xuelong Li:
Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning. CoRR abs/2404.19346 (2024) - 2023
- [j2]Chenjia Bai
Addressing Hindsight Bias in Multigoal Reinforcement Learning. IEEE Trans. Cybern. 53(1): 392-405 (2023) - [j1]Chenjia Bai
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 34(8): 4776-4790 (2023) - [c11]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency. ICLR 2023 - [c10]Sirui Zheng, Lingxiao Wang, Shuang Qiu, Zuyue Fu, Zhuoran Yang, Csaba Szepesvári, Zhaoran Wang:
Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics. ICLR 2023 - [i14]Haoran He, Chenjia Bai, Hang Lai, Lingxiao Wang, Weinan Zhang:
Privileged Knowledge Distillation for Sim-to-Real Policy Generalization. CoRR abs/2305.18464 (2023) - 2022
- [c9]Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang:
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. ICLR 2022 - [c8]Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang:
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. ICML 2022: 18168-18210 - [i13]Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang:
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. CoRR abs/2202.11566 (2022) - [i12]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency. CoRR abs/2205.13476 (2022) - [i11]Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang:
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. CoRR abs/2207.14800 (2022) - [i10]Yufeng Zhang, Boyi Liu, Qi Cai, Lingxiao Wang, Zhaoran Wang:
An Analysis of Attention via the Lens of Exchangeability and Latent Variable Models. CoRR abs/2212.14852 (2022) - 2021
- [c7]Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang:
Principled Exploration via Optimistic Bootstrapping and Backward Induction. ICML 2021: 577-587 - [c6]Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang:
Dynamic Bottleneck for Robust Self-Supervised Exploration. NeurIPS 2021: 17007-17020 - [c5]Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data. NeurIPS 2021: 21164-21175 - [i9]Chenjia Bai, Lingxiao Wang, Lei Han, Jianye Hao, Animesh Garg, Peng Liu, Zhaoran Wang:
Principled Exploration via Optimistic Bootstrapping and Backward Induction. CoRR abs/2105.06022 (2021) - [i8]Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha:
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach. CoRR abs/2105.08268 (2021) - [i7]Chenjia Bai, Lingxiao Wang, Lei Han, Animesh Garg, Jianye Hao, Peng Liu, Zhaoran Wang:
Dynamic Bottleneck for Robust Self-Supervised Exploration. CoRR abs/2110.10735 (2021) - [i6]Zhi-Hong Deng, Zuyue Fu, Lingxiao Wang, Zhuoran Yang, Chenjia Bai, Zhaoran Wang, Jing Jiang:
SCORE: Spurious COrrelation REduction for Offline Reinforcement Learning. CoRR abs/2110.12468 (2021) - 2020
- [c4]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence. ICLR 2020 - [c3]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
On the Global Optimality of Model-Agnostic Meta-Learning. ICML 2020: 9837-9846 - [c2]Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning. ICML 2020: 10092-10103 - [i5]Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning. CoRR abs/2006.11917 (2020) - [i4]Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data. CoRR abs/2006.12311 (2020) - [i3]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
On the Global Optimality of Model-Agnostic Meta-Learning. CoRR abs/2006.13182 (2020) - [i2]Chenjia Bai, Peng Liu, Zhaoran Wang, Kaiyu Liu, Lingxiao Wang, Yingnan Zhao:
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning. CoRR abs/2010.08755 (2020)
2010 – 2019
- 2019
- [c1]Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Statistical-Computational Tradeoff in Single Index Models. NeurIPS 2019: 10419-10426 - [i1]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Neural Policy Gradient Methods: Global Optimality and Rates of Convergence. CoRR abs/1909.01150 (2019)
Coauthor Index
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