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ORCIDBaoxiang Wang 0001
Person information
- affiliation: Chinese University of Hong Kong, Department of Computer Science and Engineering, Shenzhen, China
- affiliation: Shenzhen Institute of Artificial Intelligence and Robotics for Society, China
- affiliation (former): Borealis AI, Edmonton, AB, Canada
Other persons with the same name
- Baoxiang Wang 0002 — National Key Laboratory of Science and Technology on C4ISR, Nanjing, China
- Baoxiang Wang 0003 — North University of China, School of Mechanical and Power Engineering, Taiyuan, China
- Baoxiang Wang 0004 — Double Coin Group (Jiangsu) Tyre Co. Ltd, Rugao, China
- Baoxiang Wang 0005
![[0000-0002-6569-9049]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-6569-9049")
— Xi'an Jiaotong University, School of Mechanical Engineering, State Key Laboratory for Manufacturing Systems Engineering, China - Baoxiang Wang 0006 — China University of Petroleum (East China), College of Oceanography and Space Informatics, Qingdao, China
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2020 – today
- 2025
[c33]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Last-iterate Convergence in Regularized Graphon Mean Field Game. AAAI 2025: 13779-13787- [c32]Canzhe Zhao, Xiangcheng Zhang, Baoxiang Wang, Shuai Li:
Logarithmic Regret for Linear Markov Decision Processes with Adversarial Corruptions. AAAI 2025: 22759-22767 - [i40]Wenhao Li, Yue Lin, Xiangfeng Wang, Bo Jin, Hongyuan Zha, Baoxiang Wang:
Verbalized Bayesian Persuasion. CoRR abs/2502.01587 (2025) - [i39]Shuhui Zhu, Baoxiang Wang, Sriram Ganapathi Subramanian, Pascal Poupart:
Learning to Negotiate via Voluntary Commitment. CoRR abs/2503.03866 (2025) - 2024
- [j4]Fang Kong, Xiangcheng Zhang, Baoxiang Wang, Shuai Li:
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization. Trans. Mach. Learn. Res. 2024 (2024) - [j3]Dandan Guo, Chaojie Wang, Baoxiang Wang, Hongyuan Zha:
Learning Fair Representations via Distance Correlation Minimization. IEEE Trans. Neural Networks Learn. Syst. 35(2): 2139-2152 (2024) - [c31]Jiawei Xu, Cheng Zhou, Yizheng Zhang, Baoxiang Wang, Lei Han:
Relative Policy-Transition Optimization for Fast Policy Transfer. AAAI 2024: 16164-16172 - [c30]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. AISTATS 2024: 2044-2052 - [c29]Ruinan Jin, Shuai Li, Baoxiang Wang:
On Stationary Point Convergence of PPO-Clip. ICLR 2024 - [c28]Han Wang, Wenhao Li, Hongyuan Zha, Baoxiang Wang:
Carbon Market Simulation with Adaptive Mechanism Design. IJCAI 2024: 8824-8828 - [c27]Jingwei Li, Jing Dong, Can Chang, Baoxiang Wang, Jingzhao Zhang:
Online Control with Adversarial Disturbance for Continuous-time Linear Systems. NeurIPS 2024 - [c26]Ruofeng Yang, Bo Jiang, Cheng Chen, Ruinan Jin, Baoxiang Wang, Shuai Li:
Few-Shot Diffusion Models Escape the Curse of Dimensionality. NeurIPS 2024 - [c25]Jing Dong, Jingwei Li, Baoxiang Wang, Jingzhao Zhang:
Online Policy Optimization for Robust Markov Decision Process. UAI 2024: 1146-1175 - [i38]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games. CoRR abs/2404.06516 (2024) - [i37]Han Wang, Wenhao Li, Hongyuan Zha, Baoxiang Wang:
Carbon Market Simulation with Adaptive Mechanism Design. CoRR abs/2406.07875 (2024) - [i36]Jiawei Xu, Rui Yang, Feng Luo, Meng Fang, Baoxiang Wang, Lei Han:
Robust Decision Transformer: Tackling Data Corruption in Offline RL via Sequence Modeling. CoRR abs/2407.04285 (2024) - [i35]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback. CoRR abs/2408.08395 (2024) - [i34]Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama:
Adaptive complexity of log-concave sampling. CoRR abs/2408.13045 (2024) - [i33]Ruinan Jin, Xiaoyu Wang, Baoxiang Wang:
Asymptotic and Non-Asymptotic Convergence Analysis of AdaGrad for Non-Convex Optimization via Novel Stopping Time-based Analysis. CoRR abs/2409.05023 (2024) - [i32]Ruinan Jin, Xiao Li, Yaoliang Yu, Baoxiang Wang:
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD. CoRR abs/2410.04458 (2024) - [i31]Jing Dong, Baoxiang Wang, Yaoliang Yu:
Last-iterate Convergence in Regularized Graphon Mean Field Game. CoRR abs/2410.08746 (2024) - [i30]Han Wang, Binbin Chen, Tieying Zhang, Baoxiang Wang:
Learning to Construct Implicit Communication Channel. CoRR abs/2411.01553 (2024) - [i29]Nanxiang Zhou, Jing Dong, Yutian Li, Baoxiang Wang:
On the Decomposition of Differential Game. CoRR abs/2411.03802 (2024) - 2023
- [j2]Shanchao Yang, Kaili Ma, Baoxiang Wang, Tianshu Yu, Hongyuan Zha:
Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring. Trans. Mach. Learn. Res. 2023 (2023) - [c24]Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang:
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning. AAAI 2023: 11672-11680 - [c23]Wenhao Li, Baoxiang Wang, Shanchao Yang, Hongyuan Zha:
Diverse Policy Optimization for Structured Action Space. AAMAS 2023: 819-828 - [c22]Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li:
Online Influence Maximization under Decreasing Cascade Model. AAMAS 2023: 2197-2204 - [c21]Jing Dong, Li Shen, Yinggan Xu, Baoxiang Wang:
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation. AAMAS 2023: 2640-2642 - [c20]Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Shuai Li:
Learning Adversarial Linear Mixture Markov Decision Processes with Bandit Feedback and Unknown Transition. ICLR 2023 - [c19]Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li:
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning. IJCAI 2023: 4638-4646 - [c18]Jiahui Li, Kun Kuang, Baoxiang Wang, Xingchen Li, Fei Wu, Jun Xiao, Long Chen:
Two Heads are Better Than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning. NeurIPS 2023 - [c17]Yue Lin, Wenhao Li, Hongyuan Zha, Baoxiang Wang:
Information Design in Multi-Agent Reinforcement Learning. NeurIPS 2023 - [c16]Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Xuezhou Zhang, Shuai Li:
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback. NeurIPS 2023 - [c15]Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li:
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization. WSDM 2023: 985-993 - [i28]Fang Kong, Xiangcheng Zhang, Baoxiang Wang, Shuai Li:
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization. CoRR abs/2302.06834 (2023) - [i27]Wenhao Li, Baoxiang Wang, Shanchao Yang, Hongyuan Zha:
Diverse Policy Optimization for Structured Action Space. CoRR abs/2302.11917 (2023) - [i26]Yue Lin, Wenhao Li, Hongyuan Zha, Baoxiang Wang:
Information Design in Multi-Agent Reinforcement Learning. CoRR abs/2305.06807 (2023) - [i25]Wenhao Li, Dan Qiao, Baoxiang Wang, Xiangfeng Wang, Bo Jin, Hongyuan Zha:
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning. CoRR abs/2305.10865 (2023) - [i24]Fang Kong, Jize Xie, Baoxiang Wang, Tao Yao, Shuai Li:
Online Influence Maximization under Decreasing Cascade Model. CoRR abs/2305.15428 (2023) - [i23]Jingwei Li, Jing Dong, Baoxiang Wang, Jingzhao Zhang:
Online Control with Adversarial Disturbance for Continuous-time Linear Systems. CoRR abs/2306.01952 (2023) - [i22]Jing Dong, Jingyu Wu, Siwei Wang, Baoxiang Wang, Wei Chen:
Taming the Exponential Action Set: Sublinear Regret and Fast Convergence to Nash Equilibrium in Online Congestion Games. CoRR abs/2306.13673 (2023) - [i21]Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li:
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2308.09902 (2023) - [i20]Canzhe Zhao, Ruofeng Yang, Baoxiang Wang, Xuezhou Zhang, Shuai Li:
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback. CoRR abs/2311.07876 (2023) - 2022
- [j1]Jing Dong, Shiji Zhou, Baoxiang Wang, Han Zhao:
Algorithms and Theory for Supervised Gradual Domain Adaptation. Trans. Mach. Learn. Res. 2022 (2022) - [c14]Kun Wang, Jing Dong, Baoxiang Wang, Shuai Li:
Cascading Bandit Under Differential Privacy. ICASSP 2022: 4418-4422 - [c13]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao:
Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning. ICML 2022: 12843-12856 - [c12]Jing Dong, Ke Li, Shuai Li, Baoxiang Wang:
Combinatorial Bandits under Strategic Manipulations. WSDM 2022: 219-229 - [i19]Canzhe Zhao, Yanjie Ze, Jing Dong, Baoxiang Wang, Shuai Li:
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization. CoRR abs/2201.10447 (2022) - [i18]Jing Dong, Li Shen, Yinggan Xu, Baoxiang Wang:
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation. CoRR abs/2202.13863 (2022) - [i17]Jing Dong, Shiji Zhou, Baoxiang Wang, Han Zhao:
Algorithms and Theory for Supervised Gradual Domain Adaptation. CoRR abs/2204.11644 (2022) - [i16]Jing Dong, Jingwei Li, Baoxiang Wang, Jingzhao Zhang:
Online Policy Optimization for Robust MDP. CoRR abs/2209.13841 (2022) - [i15]Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang:
Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning. CoRR abs/2211.15612 (2022) - 2021
- [c11]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao:
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. KDD 2021: 934-942 - [i14]Jing Dong, Ke Li, Shuai Li, Baoxiang Wang:
Combinatorial Bandits under Strategic Manipulations. CoRR abs/2102.12722 (2021) - [i13]Kun Wang, Jing Dong, Baoxiang Wang, Shuai Li, Shuo Shao:
Cascading Bandit under Differential Privacy. CoRR abs/2105.11126 (2021) - [i12]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao:
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. CoRR abs/2106.00285 (2021) - [i11]Baoxiang Wang, Huanjian Zhou:
Multilinear extension of k-submodular functions. CoRR abs/2107.07103 (2021) - [i10]Jing Dong, Shuai Li, Baoxiang Wang:
Incentivizing an Unknown Crowd. CoRR abs/2109.04226 (2021) - [i9]Shanchao Yang, Kaili Ma, Baoxiang Wang, Hongyuan Zha:
Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient. CoRR abs/2110.09035 (2021) - [i8]Qi Tian, Kun Kuang, Baoxiang Wang, Furui Liu, Fei Wu:
Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth. CoRR abs/2112.10374 (2021) - 2020
- [c10]Baoxiang Wang, Shuai Li, Jiajin Li, Siu On Chan:
The Gambler's Problem and Beyond. ICLR 2020 - [c9]Andrej Bogdanov, Baoxiang Wang:
Learning and Testing Variable Partitions. ITCS 2020: 37:1-37:22 - [i7]Baoxiang Wang, Shuai Li, Jiajin Li, Siu On Chan:
The Gambler's Problem and Beyond. CoRR abs/2001.00102 (2020) - [i6]Andrej Bogdanov, Baoxiang Wang:
Learning and Testing Variable Partitions. CoRR abs/2003.12990 (2020)
2010 – 2019
- 2019
- [c8]Baoxiang Wang, Tongfang Sun, Xianjun Sam Zheng:
Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-Valued Inverse Reinforcement Learning. AIIDE 2019: 195-201 - [c7]Baoxiang Wang:
Recurrent Existence Determination Through Policy Optimization. IJCAI 2019: 3656-3662 - [c6]Kenny Young, Baoxiang Wang, Matthew E. Taylor:
Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control. IJCAI 2019: 4185-4191 - [c5]Baoxiang Wang, Nidhi Hegde:
Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces. NeurIPS 2019: 11323-11333 - [i5]Baoxiang Wang, Nidhi Hegde:
Private Q-Learning with Functional Noise in Continuous Spaces. CoRR abs/1901.10634 (2019) - [i4]Baoxiang Wang:
Recurrent Existence Determination Through Policy Optimization. CoRR abs/1905.13551 (2019) - 2018
- [c4]Jiajin Li, Baoxiang Wang:
Policy Optimization with Second-Order Advantage Information. ICLR (Workshop) 2018 - [c3]Jiajin Li, Baoxiang Wang, Shengyu Zhang:
Policy Optimization with Second-Order Advantage Information. IJCAI 2018: 5038-5044 - [i3]Jiajin Li, Baoxiang Wang:
Policy Optimization with Second-Order Advantage Information. CoRR abs/1805.03586 (2018) - [i2]Kenny Young, Baoxiang Wang, Matthew E. Taylor:
Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control. CoRR abs/1805.04514 (2018) - [i1]Baoxiang Wang, Tongfang Sun, Xianjun Sam Zheng:
Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning. CoRR abs/1807.00366 (2018) - 2016
- [c2]Shuai Li, Baoxiang Wang, Shengyu Zhang, Wei Chen:
Contextual Combinatorial Cascading Bandits. ICML 2016: 1245-1253 - 2015
- [c1]Cuiyun Gao, Baoxiang Wang, Pinjia He, Jieming Zhu, Yangfan Zhou, Michael R. Lyu:
PAID: Prioritizing app issues for developers by tracking user reviews over versions. ISSRE 2015: 35-45
Coauthor Index
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