Bo (Beau) Liu


Ph.D. AAAI SM, IEEE SM

[Brief Bio] [Google Scholar] [DBLP]





(Machine Learning Conference Ratings) (CS Conference Ratings 1) (CS Conference Ratings 2) (CS Conference Ratings 3)

Publications Stats

Citation: >3000, h-index: 29.
Journal   Total: 29
Transaction papers or equivalent: 12, Impact factor >2: 25
JAIR (1), IEEE-TNNLS (4), IEEE-TETCI (1), ACM-TECS (1), IET (3), AAS (1)
Conference   Total: 28
Top-tier AI/ML conferences: 26
NIPS/NeurIPS (4), ICML (3), ICLR (1), UAI (3), IJCAI (2), AAAI (5), AAMAS (3), ICLP (1), ICRA(1), UbiComp(1)

Publications

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For publications sorted by date, please use my Google Scholar profile.


RL, Control, and Multi-Agent

  • Bayes-Sufficient Compression Is Not Enough: How Communication Helps in Multi-Agent Systems?
    Y. Xie, Z. Zhou, Y. Fan, Y. Ge, B. Han, Bo Liu
    Advances in Neural Information Processing Systems (NeurIPS), 2026 [PDF]

  • TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination.
    Y. Xie, S. Liu, F. Fan, Y. Yao, S. Cao, Y. Zhao, Bo Liu
    Proceedings of the International Conference on Machine Learning (ICML), 2026 [code]

  • SAT: Sequential Agent Tuning for Coordinator-Free Plug-and-Play Multi-LLM Training with Monotonic Improvement Guarantees.
    Yi Xie, Yangyang Xu, Yi Fan, Bo Liu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2026 [code]

  • OpenCDA-MARL: A Unified Benchmarking Framework for Cooperative Autonomous Intersection Management with Multi-Agent Reinforcement Learning.
    Lihao Guo, Louis Liu, Jiahao Tang, Bo Liu, Siyang Cao
    IEEE Robotics and Automation Letters (IEEE RA-L), 2026 [code]

  • OPRIDE: Efficient Offline Preference-based Reinforcement Learning via In-Dataset Exploration.
    Yiqin Yang, Hao Hu, Yihuan Mao, Jin Zhang, Chengjie Wu, Xu Yang, Runpeng Xie, Yi Fan, Bo Liu, Yang Gao, Bo Xu, Chongjie Zhang
    The Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026

  • From Past to Future: Rethinking Eligibility Traces.
    Dhawal Gupta, Scott Jordan, Shreyas Chaudhari, B. Liu, Philip Thomas, Bruno C. da Silva
    Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024

  • Offline Reinforcement Learning for Price-Based Demand Response Program Design.
    Ce Xu, B. Liu, Yue Zhao
    Proc. the 57th Annual Conference on Information Sciences and Systems (CISS), 2023

  • TOPS: Transition-based volatility-reduced policy search.
    Liangliang Xu, Daoming Lyu, Yangchen Pan, Aiwen Jiang, B. Liu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022
    Best and Visionary Paper Award

  • TDM: Trustworthy Decision-Making via Interpretability Enhancement.
    Daoming Lyu, Fangkai Yang, Hugh Kwon, Wen Dong, Levent Yilmaz, B. Liu
    IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE-TETCI), 2022
    This paper builds up a trustworthy decision-making framework with novel trust evaluation and explainability enhancement methods. [code-Tensorflow] [code-PyTorch]

  • Tutorial: Risk-averse Reinforcement Learning: Algorithms And Meta-algorithms.
    B. Liu, Bo An, Yangyang Xu
    Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022

  • Tutorial: Efficient Neural-Symbolic Reasoning via Reinforcement Learning.
    Daoming Lyu, B. Liu, Jianshu Chen, Akshat Kumar, Jiajing Ling
    32nd International Conference on Automated Planning and Scheduling (ICAPS), 2022

  • Tutorial: Risk-aware Single-agent & Multi-agent Reinforcement Learning: Algorithms and Meta-algorithms.
    B. Liu, Bo An, Yangyang Xu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022

  • Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning.
    Zhang, S., B. Liu, Whiteson, S.
    35th AAAI Conference on Artificial Intelligence (AAAI), 2021
    The first meta-framework that can "ROBUSTIFY" your vanilla RL algorithm.

  • Explainable Neuro-Symbolic Hierarchical Reinforcement Learning.
    Daoming Lyu, Fangkai Yang, Hugh Kwon, B. Liu, Wen Dong, Levent Yilmaz
    Neuro-Symbolic Artificial Intelligence: The State of the Art (book chapter), 2021 [code-Tensorflow] [code-PyTorch]

  • Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation.
    Zhang, S., B. Liu, Yao, H., Whiteson, S.
    International Conference on Machine Learning (ICML), 2020

  • GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values.
    Zhang, S., B. Liu, Whiteson, S.
    International Conference on Machine Learning (ICML), 2020

  • A Block Coordinate Ascent Algorithm for Mean-Variance Optimization.
    B. Liu*, T. Xie* (* equal contribution), Y. Xu, M. Ghavamzadeh, Y. Chow, D. Lyu, D. Yoon
    32nd Conference on Neural Information Processing Systems (NIPS), 2018

  • A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming.
    D. Lyu, F. Yang, B. Liu, S. Gustafson
    35th International Conference on Logic Programming (ICLP), 2019

  • SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning.
    D. Lyu, F. Yang, B. Liu, S. Gustafson
    33rd AAAI Conference on Artificial Intelligence (AAAI), 2019 [code-Tensorflow] [code-PyTorch]

  • PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making.
    F. Yang, D. Lyu, B. Liu, S. Gustafson
    27th International Joint Conference on Artificial Intelligence (IJCAI), 2018 [code-Tensorflow] [code-PyTorch]

  • Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample Complexity.
    B. Liu, I. Gemp, M. Ghamvamzadeh, J. Liu, S. Mahadevan, and M. Petrik
    Journal of Artificial Intelligence Research (JAIR), 2018. (Journal version of our 2014 arxiv paper with extended results.)
    • [code]
  • Proximal Gradient Temporal Difference Learning Algorithms.
    B. Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik.
    25th International Joint Conference on Artificial Intelligence (IJCAI), New York City, 2016
    • [code]
  • Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning.
    B. Liu, L Zhang, J Liu.
    32nd Conference on Uncertainty in Artificial Intelligence (UAI), Jersey City, NJ, 2016

  • Finite-Sample Analysis of Proximal Gradient TD Algorithms.
    B. Liu, J Liu, M Ghavamzadeh, S Mahadevan, M Petrik.
    31st Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam, The Netherlands, July 12-16, 2015, Facebook Best Student Paper Award. [ppt] [video] [code]
    The first paper giving sample complexity analysis of RL algorithms with linear computational cost per step.

  • Proximal Reinforcement Learning: A New Theory of Sequential Decision Making in Primal-Dual Spaces.
    S Mahadevan, B. Liu, P Thomas, W Dabney, S Giguere, N Jacek, I Gemp, J Liu
    arXiv preprint arXiv:1405.6757, 2014
    The first paper setting up a stochastic optimization framwork for TD learning using Legendre-Fenchel duality and proximal operators, and pointing out GTD algorithm is a saddle-point algorithm.
    • [code]
  • Regularized Off-Policy TD-Learning.
    B. Liu,     S Mahadevan, J Liu.
    26th Annual Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, 2012, December 3-6, Spotlight Presentation (5% acceptance). [ppt] [video]
    • The first paper introducing saddle-point formulation into TD learning and Reinforcement Learning.

  • Sparse Q-learning with Mirror Descent.
    S Mahadevan, B. Liu.
    28th Conference on Uncertainty in Artificial Intelligence (UAI), August 15-17, 2012, Catalina Island, CA. [ppt]

  • Compressive Reinforcement Learning with Oblique Random Projections.
    B. Liu    , S Mahadevan.
    Univ. of Massachusetts Technical Report UM-CS-2011-024.

  • Basis Construction from Power Series Expansions of Value Functions.
    S Mahadevan, B. Liu.
    24th Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada, 2010, December 6-8. [ppt]

  • Two-time-scale online actor-critic paradigm driven by POMDP.
    B. Liu, H He, DW Repperger
    International Conference on Networking, Sensing and Control (ICNSC), 2010.

    • Best Paper Award Nominee

    Trustworthy AI

  • SAT: Sequential Agent Tuning for Coordinator-Free Plug-and-Play Multi-LLM Training with Monotonic Improvement Guarantees.
    Yi Xie, Yangyang Xu, Yi Fan, Bo Liu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2026 [code]

  • DiFusionSeg: Diffusion-Driven Semantic Segmentation with Multi-Modal Image Fusion for Enhanced Perception.
    Zhiwei Wang, Defeng He, Li Zhao, Bo Liu, Yayu Zheng, Xiaoqin Zhang
    Knowledge-Based Systems, Elsevier, 2025

  • Self-supervised multi-scale pyramid fusion networks for realistic bokeh effect rendering.
    Zhifeng Wang, Aiwen Jiang, Chunjie Zhang, Hanxi Lia, B. Liu
    Journal of Visual Communication and Image Representation, 2022

  • A Critical Review of Inductive Logic Programming Techniques for Explainable AI.
    Zheng Zhang, Levent Yilmaz, B. Liu
    IEEE Transactions on Neural Networks and Learning Systems (IEEE-TNNLS), 2024

  • Contrastive topic-enhanced network for video captioning.
    Yawen Zeng, Yiru Wang, Dongliang Liao, Gongfu Li, Jin Xu, Xiangmin Xu, B. Liu, Hong Man
    Expert Systems with Applications (ESA), 2024

  • TDM: Trustworthy Decision-Making via Interpretability Enhancement.
    Daoming Lyu, Fangkai Yang, Hugh Kwon, Wen Dong, Levent Yilmaz, B. Liu
    IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE-TETCI), 2022
    This paper builds up a trustworthy decision-making framework with novel trust evaluation and explainability enhancement methods. [code-Tensorflow] [code-PyTorch]

  • Tutorial: Risk-averse Reinforcement Learning: Algorithms And Meta-algorithms.
    B. Liu, Bo An, Yangyang Xu
    Conference on Uncertainty in Artificial Intelligence (UAI), Eindhoven, Netherlands, 2022

  • Tutorial: Efficient Neural-Symbolic Reasoning via Reinforcement Learning.
    Daoming Lyu, B. Liu, Jianshu Chen, Akshat Kumar, Jiajing Ling
    32nd International Conference on Automated Planning and Scheduling (ICAPS), 2022

  • Tutorial: Risk-aware Single-agent & Multi-agent Reinforcement Learning: Algorithms and Meta-algorithms.
    B. Liu, Bo An, Yangyang Xu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022

  • Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning.
    Zhang, S., B. Liu, Whiteson, S.
    35th AAAI Conference on Artificial Intelligence (AAAI), 2021
    The first meta-framework that can "ROBUSTIFY" your vanilla RL algorithm.

  • Explainable Neuro-Symbolic Hierarchical Reinforcement Learning.
    Daoming Lyu, Fangkai Yang, Hugh Kwon, B. Liu, Wen Dong, Levent Yilmaz
    Neuro-Symbolic Artificial Intelligence: The State of the Art (book chapter), 2021 [code-Tensorflow] [code-PyTorch]

  • Ensemble single image deraining network via progressive structural boosting constraints.
    Long Peng, Aiwen Jiang, Haoran Wei, B. Liu, Mingwen Wang
    Signal Processing: Image Communication, Elsevier, 2021

  • A Lightweight Multi-scale Aggregated Model for Detecting Aerial Images Captured by UAVs.
    Zhaokun Li, Xueliang Liu, Ye Zhao, B. Liu, Zhen Huang, Richang Hong
    Journal of Visual Communication and Image Representation, 2021

  • Crowd understanding and analysis.
    Qi Wang, B. Liu, Jianzhe Lin
    IET Image Processing (IET-IP), 2021

  • Model Credibility Revisited: Concepts and Considerations for Appropriate Trust.
    Levent Yilmaz, B. Liu
    Journal of Simulation (JoS), 2020

  • A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming.
    D. Lyu, F. Yang, B. Liu, S. Gustafson
    35th International Conference on Logic Programming (ICLP), 2019

  • SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning.
    D. Lyu, F. Yang, B. Liu, S. Gustafson
    33rd AAAI Conference on Artificial Intelligence (AAAI), [code-Tensorflow] [code-PyTorch]

  • PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making.
    F. Yang, D. Lyu, B. Liu, S. Gustafson
    27th International Joint Conference on Artificial Intelligence (IJCAI), 2018 [code-Tensorflow] [code-PyTorch]

    • Agentic AI

  • Bayes-Sufficient Compression Is Not Enough: How Communication Helps in Multi-Agent Systems?
    Y. Xie, Z. Zhou, Y. Fan, Y. Ge, B. Han, Bo Liu
    Advances in Neural Information Processing Systems (NeurIPS), 2026 [PDF]

  • SAT: Sequential Agent Tuning for Coordinator-Free Plug-and-Play Multi-LLM Training with Monotonic Improvement Guarantees.
    Yi Xie, Yangyang Xu, Yi Fan, Bo Liu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2026 [code]

  • TeamTR: Trust-Region Fine-Tuning for Multi-Agent LLM Coordination.
    Y. Xie, S. Liu, F. Fan, Y. Yao, S. Cao, Y. Zhao, Bo Liu
    Proceedings of the International Conference on Machine Learning (ICML), 2026 [code]

  • OPRIDE: Efficient Offline Preference-based Reinforcement Learning via In-Dataset Exploration.
    Yiqin Yang, Hao Hu, Yihuan Mao, Jin Zhang, Chengjie Wu, Xu Yang, Runpeng ..., Bo Liu
    The Fourteenth International Conference on Learning Representations (ICLR), 2026

  • OpenCDA-MARL: A Unified Benchmarking Framework for Cooperative Autonomous Intersection Management with Multi-Agent Reinforcement Learning.
    Lihao Guo, Louis Liu, Jiahao Tang, Bo Liu, Siyang Cao
    IEEE Robotics and Automation Letters (IEEE RA-L), 2026 [code]

  • TDM: Trustworthy Decision-Making via Interpretability Enhancement.
    Daoming Lyu, Fangkai Yang, Hugh Kwon, Wen Dong, Levent Yilmaz, B. Liu
    IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE-TETCI), 2022
    This paper builds up a trustworthy decision-making framework with novel trust evaluation and explainability enhancement methods. [code-Tensorflow] [code-PyTorch]

  • Tutorial: Efficient Neural-Symbolic Reasoning via Reinforcement Learning.
    Daoming Lyu, B. Liu, Jianshu Chen, Akshat Kumar, Jiajing Ling
    32nd International Conference on Automated Planning and Scheduling (ICAPS), 2022

  • Tutorial: Risk-aware Single-agent & Multi-agent Reinforcement Learning: Algorithms and Meta-algorithms.
    B. Liu, Bo An, Yangyang Xu
    International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2022

  • Explainable Neuro-Symbolic Hierarchical Reinforcement Learning.
    Daoming Lyu, Fangkai Yang, Hugh Kwon, B. Liu, Wen Dong, Levent Yilmaz
    Neuro-Symbolic Artificial Intelligence: The State of the Art (book chapter), 2021 [code-Tensorflow] [code-PyTorch]

  • A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming.
    D. Lyu, F. Yang, B. Liu, S. Gustafson
    35th International Conference on Logic Programming (ICLP), 2019

  • SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning.
    D. Lyu, F. Yang, B. Liu, S. Gustafson
    33rd AAAI Conference on Artificial Intelligence (AAAI), 2019 [code-Tensorflow] [code-PyTorch]

  • PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making.
    F. Yang, D. Lyu, B. Liu, S. Gustafson
    27th International Joint Conference on Artificial Intelligence (IJCAI), 2018 [code-Tensorflow] [code-PyTorch]