Bo Liu


Assistant Professor
Department of Computer Science and Software Engineering
3101P Shelby Center for Engineering Technology
Auburn University
Auburn, AL 36849-5347
Email:
firstname(nospace)lastname AT schoolname DOT edu

[Google Scholar] [DBLP]


Publications

Sort by

2019

  • 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), Las Cruces, NM, 2019.

  • Systems and methods for neural clinical paraphrase generation.
    Sadid Hasan. S., B. Liu, O. Farri Farri, Junyi Liu, & Aaditya Prakash.
    U.S. Patent Application No. 16/072,128, 2019

  • Optimal Control of Complex Systems through Variational Inference with a Discrete Event Decision Process.
    F. Yang, B. Liu, W. Dong
    Autonomous Agents and Multi-agent Systems (AAMAS), Montreal, Canada, 2019

  • Deep Residual Refining based Pseudo Multi-frame Network for Effective Single Image Super Resolution.
    K. Mei, A. Jiang, J. Li, B. Liu, M. Wang
    IET Image Processing (IET-IP), 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), Honolulu, HI, 2019.
  • This paper gains the most state-of-the-art result on Montezuma's Revenge with interpretability at the task level. This is one of the first work towards human-interpretable data-driven decision-making!

  • QUOTA: The Quantile Option Architecture for Reinforcement Learning.
    S. Zhang, B. Mavrin, L. Kong, B. Liu, H. Yao
    33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, HI, 2019.

  • Hierarchical Feature Selection for Random Projection.
    Wang, Q.; Wan, J.; Nie, F.; B. Liu; Young, C.; Li, X
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNN), 2019

  • 2018

  • 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), Montreal, CA, 2018
  • The first risk-sensitive policy search algorithm with single time-scale and sample complexity analysis. It is also the first time introducing coordinate descent/ascent formulation into Reinforcement Learning.
    * reads: Co-primary authors with equal contributions. The authorship is in either alphabetic or reverse alphabetic order.

  • A Novel Restoration Algorithm for Noisy Complex Illumination.
    S. Li, Z. Liu, T. Gao, F. Kong, Z. Jiao, A, Yang, B. Liu
    IET Computer Vision (IET-CV), 2018

  • Stable and Efficient Policy Evaluation.
    D. Lyu, B. Liu, M. Geist, W. Dong, S. Biaz, and Q. Wang
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNN), 2019

  • 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.)

  • 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), Stockholm, Sweden, 2018. Press Coverage [ppt] [poster] [video]

  • 2017

  • Deep Multimodal Reinforcement Network with Contextually Guided Recurrent Attention for Image Question Answering.
    Jiang, A. W., B. Liu, & Wang, M. W.
    Journal of Computer Science and Technology, 32(4), 738-748, 2017

  • Neural Clinical Paraphrase Generation with Attention.
    Hasan, S. A., B. Liu, Liu, J. et.al.
    Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), 2017

  • 2016

  • 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

  • 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

  • Uncorrelated Group LASSO.
    D Kong, J Liu, B. Liu, X Bao.
    30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, AZ, Feb 12-17, 2016

  • 2015

  • 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]
    The first paper giving sample complexity analysis of RL algorithms with linear computational cost per step.

  • 2014

  • 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.

  • Bluetooth aided mobile phone localization: a nonlinear neural circuit approach.
    S Li, Y Lou, B. Liu
    ACM Transactions on Embedded Computing Systems (ACM TECS), 2014

  • 2013

  • Selective Positive-Negative Feedback Produces the Winner-Take-All Competition in Recurrent Neural Networks.
    S Li, B. Liu, Y Li
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNN) 24, 301-309, 2013

  • Neural network based mobile phone localization using Bluetooth connectivity.
    S Li, B. Liu, B Chen, Y Lou
    Neural Computing & Applications, 2013


  • A Nonlinear Model to Generate the Winner-take-all Competition.
    S Li, Y Wang, J Yu, B. Liu
    Communications in Nonlinear Science and Numerical Simulation, 2013

  • 2012

  • 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]

  • Sparse Manifold Alignment.
    B. Liu, C Wang, H Vu, S Mahadevan.
    Univ. of Massachusetts Technical Report UM-CS-2012-030.

  • Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks.
    S Li, S Chen, B. Liu, Y Li, Y Liang
    Neurocomputing, 2012, One of the most-cited Neurocomputing paper since 2012 according to Scopus

  • 2011

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

  • 2010

  • 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.

  • Adaptive Dual Network Design for a Class of SIMO Systems with Nonlinear Time-variant Uncertainties.
    B. Liu
    , HB He, S Chen
    Acta Automatica Sinica 36 (4), 564-572, 2010

  • Reinforcement Learning and Decision-Making

  • 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), Las Cruces, NM, 2019.

  • Optimal Control of Complex Systems through Variational Inference with a Discrete Event Decision Process.
    F. Yang, B. Liu, W. Dong
    Autonomous Agents and Multi-agent Systems (AAMAS), Montreal, Canada, 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), Honolulu, HI, 2019.
  • This paper gains the most state-of-the-art result on Montezuma's Revenge with interpretability at the task level. This is one of the first work towards human-interpretable data-driven decision-making!

  • QUOTA: The Quantile Option Architecture for Reinforcement Learning.
    S. Zhang, B. Mavrin, L. Kong, B. Liu, H. Yao
    33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, HI, 2019.

  • 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), Montreal, CA, 2018
  • The first risk-sensitive policy search algorithm with single time-scale and sample complexity analysis. It is also the first time introducing coordinate descent/ascent formulation into Reinforcement Learning.
    * reads: Co-primary authors with equal contributions. The authorship is in either alphabetic or reverse alphabetic order.

  • Stable and Efficient Policy Evaluation.
    D. Lyu, B. Liu, M. Geist, W. Dong, S. Biaz, and Q. Wang
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNN), 2019

  • 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), Stockholm, Sweden, 2018. Press Coverage [ppt] [poster] [video]

  • 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.)

  • 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

  • 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]
    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.

  • 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.

  • Robotics

  • Bluetooth aided mobile phone localization: a nonlinear neural circuit approach.
    S Li, Y Lou, B. Liu
    ACM Transactions on Embedded Computing Systems (ACM TECS), 2014

  • Selective Positive-Negative Feedback Produces the Winner-Take-All Competition in Recurrent Neural Networks.
    S Li, B. Liu, Y Li
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNN) 24, 301-309, 2013

  • Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks.
    S Li, S Chen, B. Liu, Y Li, Y Liang
    Neurocomputing, 2012, One of the most-cited Neurocomputing paper since 2012 according to Scopus


  • Neural network based mobile phone localization using Bluetooth connectivity.
    S Li, B. Liu, B Chen, Y Lou
    Neural Computing & Applications, 2012

  • Adaptive Dual Network Design for a Class of SIMO Systems with Nonlinear Time-variant Uncertainties.
    B. Liu
    , HB He, S Chen
    Acta Automatica Sinica 36 (4), 564-572, 2010

  • Transparency/Explainability

  • 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), Las Cruces, NM, 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), Honolulu, HI, 2019.
  • This paper gains the most state-of-the-art result on Montezuma's Revenge with interpretability at the task level. This is one of the first work towards human-interpretable data-driven decision-making!

  • 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), Stockholm, Sweden, 2018. Press Coverage [ppt] [poster] [video]

  • Safety and Risk-Awareness

  • 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), Montreal, CA, 2018
  • The first risk-sensitive policy search algorithm with single time-scale and sample complexity analysis. It is also the first time introducing coordinate descent/ascent formulation into Reinforcement Learning.
    * reads: Co-primary authors with equal contributions. The authorship is in either alphabetic or reverse alphabetic order.

    Robust and Adaptiveness

  • 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.)

  • Stable and Efficient Policy Evaluation.
    D. Lyu, B. Liu, M. Geist, W. Dong, S. Biaz, and Q. Wang
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNN), 2019

  • 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

  • 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]
    The first paper giving sample complexity analysis of RL algorithms with linear computational cost per step.

  • Privacy-Preserving

    in progress.

    Fairness

    in progress.

    Learning Theory and Optimization

  • Hierarchical Feature Selection for Random Projection.
    Wang, Q.; Wan, J.; Nie, F.; B. Liu; Young, C.; Li, X
    IEEE Transactions on Neural Networks and Learning Systems (IEEE TNN), 2019

  • 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

  • Uncorrelated Group LASSO.
    D Kong, J Liu, B. Liu, X Bao.
    30th AAAI Conference on Artificial Intelligence (AAAI), Phoenix, AZ, Feb 12-17, 2016

  • Sparse Manifold Alignment.
    B. Liu, C Wang, H Vu, S Mahadevan.
    Univ. of Massachusetts Technical Report UM-CS-2012-030.

  • A Nonlinear Model to Generate the Winner-take-all Competition.
    S Li, Y Wang, J Yu, B. Liu
    Communications in Nonlinear Science and Numerical Simulation, 2012

  • Healthcare

  • Neural Clinical Paraphrase Generation with Attention.
    Hasan, S. A., B. Liu, Liu, J. et.al.
    Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), 2017

  • Systems and methods for neural clinical paraphrase generation.
    Sadid Hasan. S., B. Liu, O. Farri Farri, Junyi Liu, & Aaditya Prakash.
    U.S. Patent Application No. 16/072,128, 2019

  • Computer Vision

  • A Novel Restoration Algorithm for Noisy Complex Illumination.
    S. Li, Z. Liu, T. Gao, F. Kong, Z. Jiao, A, Yang, B. Liu
    IET Computer Vision (IET-CV), 2018

  • Deep Residual Refining based Pseudo Multi-frame Network for Effective Single Image Super Resolution.
    K. Mei, A. Jiang, J. Li, B. Liu, M. Wang
    IET Image Processing (IET-IP), 2019

  • Deep Multimodal Reinforcement Network with Contextually Guided Recurrent Attention for Image Question Answering.
    Jiang, A. W., B. Liu, & Wang, M. W.
    Journal of Computer Science and Technology, 32(4), 738-748, 2017