Baoxiang Wang

Orcid: 0000-0002-2997-0970

Affiliations:
  • Chinese University of Hong Kong, Department of Computer Science and Engineering, Shenzhen, China
  • Shenzhen Institute of Artificial Intelligence and Robotics for Society, China
  • Borealis AI, Edmonton, AB, Canada (former)


According to our database1, Baoxiang Wang authored at least 41 papers between 2015 and 2024.

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Timeline

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Bibliography

2024
Learning Fair Representations via Distance Correlation Minimization.
IEEE Trans. Neural Networks Learn. Syst., February, 2024

Relative Policy-Transition Optimization for Fast Policy Transfer.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback.
CoRR, 2023

Taming the Exponential Action Set: Sublinear Regret and Fast Convergence to Nash Equilibrium in Online Congestion Games.
CoRR, 2023

Online Control with Adversarial Disturbance for Continuous-time Linear Systems.
CoRR, 2023

Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning.
CoRR, 2023

Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization.
CoRR, 2023

Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization.
Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, 2023

Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Information Design in Multi-Agent Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Two Heads are Better Than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Learning Adversarial Linear Mixture Markov Decision Processes with Bandit Feedback and Unknown Transition.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Diverse Policy Optimization for Structured Action Space.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Online Influence Maximization under Decreasing Cascade Model.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation.
Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Algorithms and Theory for Supervised Gradual Domain Adaptation.
Trans. Mach. Learn. Res., 2022

Online Policy Optimization for Robust MDP.
CoRR, 2022

Combinatorial Bandits under Strategic Manipulations.
Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning.
Proceedings of the International Conference on Machine Learning, 2022

Cascading Bandit Under Differential Privacy.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth.
CoRR, 2021

Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient.
CoRR, 2021

Incentivizing an Unknown Crowd.
CoRR, 2021

Multilinear extension of k-submodular functions.
CoRR, 2021

Cascading Bandit under Differential Privacy.
CoRR, 2021

Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

2020
Learning and Testing Variable Partitions.
Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

The Gambler's Problem and Beyond.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Private Q-Learning with Functional Noise in Continuous Spaces.
CoRR, 2019

Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Recurrent Existence Determination Through Policy Optimization.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-Valued Inverse Reinforcement Learning.
Proceedings of the Fifteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2019

2018
Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning.
CoRR, 2018

Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control.
CoRR, 2018

Policy Optimization with Second-Order Advantage Information.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Policy Optimization with Second-Order Advantage Information.
Proceedings of the 6th International Conference on Learning Representations, 2018

2016
Contextual Combinatorial Cascading Bandits.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
PAID: Prioritizing app issues for developers by tracking user reviews over versions.
Proceedings of the 26th IEEE International Symposium on Software Reliability Engineering, 2015


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