Recruitment

About Me

I am currently working as a assistant professor at the Chinese University of Hong Kong, Shenzhen (CUHK-SZ).

Research Interests: Reinforcement Learning (RL) and the related topics, including, Inverse Constrained RL, Model-based RL, Interpretable RL, Agent Evaluation and Embedding. I have also studied applications on Natural Language Process (NLP), Sports analytic and Autonomous Driving.

Working Experience and Education: I have worked as a postdoctoral fellow at the University of Waterloo and Vector Institute with Prof. Pascal Poupart (2020-2022). I received my Ph.D. degree at the School of Computing Science, Simon Fraser University under the supervision of Prof. Oliver Schulte (2016-2020). I have finished a research internships at Cognitive Computing Lab, Baidu Research (supervised by Prof. Ping Li). I received my Bachelor degree at the School of Computing Science and Engineering, South China University of Technology.

Selected Publications

• Benchmarking Constraint Inference in Inverse Reinforcement Learning.
  Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2023.
  [Paper (PDF)](submitted version),
• Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game.
  Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart.
  Advances in Neural Information Processing Systems (Neurips) 2022.
  [Paper (PDF)], [Slides], [Code (To be released)],
• Learning Object-Oriented Dynamics for Planning from Text.
  Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2022.
  [Paper (PDF)], [Slides], [Code],
• Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning.
  Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart.
  Advances in Neural Information Processing Systems (Neurips) 2021.
  [Paper (PDF)], [Slides], [Code],
• Learning Agent Representations for Ice Hockey.
  Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan.
  Advances in Neural Information Processing Systems (Neurips) 2020.
  [Paper (PDF)], [Slides], [Video], [Code ],

Submitted Papers

• An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient.
  Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan.
• Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation.
  Rongyu Zhang, Xiaowei Chi, Guiliang Liu, Wenyi Zhang, Fangxin Wang.
• Cluster-driven Federated Recommendation System with Biased Message Dropout.
  Rongyu Zhang, Yun Chen, Guiliang Liu, Fangxin Wang.
• Prior and Prediction Inverse Kernel Transformer for Single Image Defocus Deblurring.
  Peng Tang, zhiqiang xu, Xin Cao, Guiliang Liu, Tobias Lasser

All Publications

• Benchmarking Constraint Inference in Inverse Reinforcement Learning.
  Guiliang Liu, Yudong Luo, Ashish Gaurav, Kasra Rezaee, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2023.
  [Paper (PDF)](submitted version),
• Learning Soft Constraints From Constrained Expert Demonstrations.
  Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2023.
  [Paper (PDF)] (submitted version),
• NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge.
  Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart.
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
• Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game.
  Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart.
  Advances in Neural Information Processing Systems (Neurips) 2022.
  [Paper (PDF)], [Slides], [Code (To be released)],
• Learning Object-Oriented Dynamics for Planning from Text.
  Guiliang Liu, Ashutosh Adhikari, Amir-massoud Farahmand, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2022.
  [Paper (PDF)], [Slides], [Code],
• Distributional Reinforcement Learning with Monotonic Splines.
  Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2022.
  [Paper (PDF)], [Code],
• Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning.
  Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart.
  Advances in Neural Information Processing Systems (Neurips) 2021.
  [Paper (PDF)], [Slides], [Code],
• Learning Agent Representations for Ice Hockey.
  Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan.
  Advances in Neural Information Processing Systems (Neurips) 2020.
  [Paper (PDF)], [Slides], [Video], [Code ],
• An Advantage Actor-Critic Algorithm with Confidence Exploration for Open Information Extraction.
  Guiliang Liu, Xu Li, Mingming Sun, Ping Li.
  SIAM International Conference on Data Mining (SDM) 2020.
  [Paper (PDF)]
• Extracting Knowledge from Web Text with Monte Carlo Tree Search.
  Guiliang Liu, Xu Li, Jiakang Wang, Mingming Sun, Ping Li.
  The ACM Web Conference (WWW) 2020.
  [Paper (PDF)], [Slides]
• Cracking the Black Box: Distilling Deep Sports Analytics.
  Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu.
  SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2020.
  [Paper (PDF)], [arXiv], [Slides], [ Video], [Code]
• Deep soccer analytics: Learning an action-value function for evaluating soccer players.
  Guiliang Liu, Yudong Luo, Oliver Schulte, and Tarak Kharrat.
  ECML-PKDD 2020 Journal Track, published at Data Mining and Knowledge Discovery (DMKD).
  [Paper (PDF)], [Slides],
• Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation.
  Guiliang Liu, Oliver Schulte.
  International Joint Conference on Artificial Intelligence (IJCAI) 2018.
  [Paper (PDF)], [arXiv], [Code], [Slides],
• Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.
  Guiliang Liu, Oliver Schulte, Wang Zhu,
  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Database (ECML-PKDD) 2018.
  [Paper (PDF)], [arXiv], [Slides],

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