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],