Selected Publications

* indicates the corresponding author. # indicates co-first author.

Year 2025

• DexSim: Automating Data Scaling for Sim2Real Generalizable Robot Control
  Guiliang Liu#, Yueci Deng#, Runyi Zhao, Huayi Zhou, Jian Chen, Jietao Chen, Ruiyan Xu, Yunxin Tai, Kui Jia.
  International Conference on Machine Learning (ICML) 2025.
  [Paper (Coming Soon)],
• Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
  Bo Yue, Jian Li, Guiliang Liu*
  International Conference on Machine Learning (ICML) 2025.
  [Paper (Coming Soon)],
• Unimodal Training-Multimodal Prediction:Cross-modal Federated Learning with Hierarchical Aggregation
  Rongyu Zhang, Xiaowei Chi, Guiliang Liu, Wenyi Zhang, Yuan Du, Fangxin Wang
  Transactions on Mobile Computing (TMC) 2025.
  [Paper (Coming Soon)],
• You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations
  Huayi Zhou, Ruixiang Wang, Yunxin Tai, Yueci Deng, Guiliang Liu, Kui Jia.
  Robotics: Science and Systems (RSS) 2025.
  [Paper], [Code],
• Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions
  Quanyuan Ruan, Jiabao Lei, Wenhao Yuan, Yanglin Zhang, Dekun Lu, Guiliang Liu*, Kui Jia*.
  IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2025.
  [Paper],
• A Distributional Approach to Uncertainty-Aware Preference Alignment Using Offline Demonstrations
  Sheng Xu, Bo Yue, Hongyuan Zha, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2025.
  [Paper (PDF)],
• Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers
  Runyi Zhao, Sheng Xu, Bo Yue, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2025.
  [Paper (PDF)],
• Understanding Constraint Inference in Safety-Critical Inverse Reinforcement Learning
  Bo Yue, Shufan Wang, Ashish Gaurav, Jian Li, Pascal Poupart, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2025.
  [Paper (PDF)],

Year 2024

• A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
  Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart
  Transactions on Machine Learning Research (TMLR) 2024.
  [Paper (PDF)]
• Modelling Competitive Behaviors in Autonomous Driving Under Generative World Model
  Guanren Qiao, Guorui Quan, Rongxiao Qu, Guiliang Liu*
  European Conference on Computer Vision (ECCV) 2024.
  [Paper (PDF)], [Code],
• Learning Constraints from Offline Demonstrations via Superior Distribution Correction Estimation
  Guorui Quan, zhiqiang xu, Guiliang Liu*.
  International Conference on Machine Learning (ICML) 2024.
  [Paper (PDF)], [Code],
• Robust Inverse Constrained Reinforcement Learning under Model Misspecification
  Sheng Xu, Guiliang Liu*.
  International Conference on Machine Learning (ICML) 2024.
  [Paper (PDF)], [Code],
• Confidence Aware Inverse Constrained Reinforcement Learning
  Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi, Kasra Rezaee, Pascal Poupart.
  International Conference on Machine Learning (ICML) 2024.
  [Paper (PDF)], [Code],
• Uncertainty-aware Constraint Inference in Inverse Constrained Reinforcement Learning
  Sheng Xu, Guiliang Liu*.
  International Conference on Learning Representations (ICLR) 2024.
  [Paper (PDF)], [Code],
• Risk, Reward, and Reinforcement Learning in Hockey Analytics
  Sheng Xu, Oliver Schulte, Yudong Luo, Pascal Poupart, Guiliang Liu.
  Invited Book Chapter in Artificial intelligence and machine learning in sports science 2024.
  [Book],

Year 2023

• Multi-Modal Inverse Constrained Reinforcement Learning from a Mixture of Demonstrations
  Guanren Qiao, Guiliang Liu*, Pascal Poupart, Zhiqiang Xu.
  Advances in Neural Information Processing Systems (NeurIPS) 2023.
  [Paper (PDF)], [Code],
• An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradient.
  Yudong Luo, Guiliang Liu, Pascal Poupart, Yangchen Pan.
  Advances in Neural Information Processing Systems (NeurIPS) 2023.
  [Paper (PDF)], [Slides], [Code],
• 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)], [Slides], [Code],
• Learning Soft Constraints From Constrained Expert Demonstrations.
  Ashish Gaurav, Kasra Rezaee, Guiliang Liu, Pascal Poupart.
  International Conference on Learning Representations (ICLR) 2023.
  [Paper (PDF)],
• NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge.
  Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart.
  International Conference on Artificial Intelligence and Statistics (AISTATS) 2023.
  [Paper (PDF)],

Year 2022 and Before

• 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],