Selected Journal Publication

(* Corresponding author)

  • [TIT] X.-Y. Liu, S. Aeron, V. Aggarwal*, X. Wang. Low-tubal-rank tensor completion using alternating minimization. IEEE Transactions on Information Theory, 2020.
  • [TNNLS] X.-Y. Liu, X. Wang, B. Yuan, J. Han. Spectral Tensor Layers for Communication-free Distributed Deep Learning. IEEE Transactions on Neural Networks and Learning Systems, 2024.
  • [TNNLS] X.-Y. Liu, Q. Huang, X. Han, B. Wu, L. Kong, A. Walid, and X. Wang*. Real-time decoding of snapshot compressive imaging using tensor FISTA-net. IEEE Transactions on Neural Networks and Learning Systems, 2023.
  • [TNNLS] X.-Y. Liu, X. Wang*. Real-time indoor localization for smartphones using tensor-generative adversarial nets. IEEE Transactions on Neural Networks and Learning Systems, 2020.
  • [MLJ] Xiao-Yang Liu, Z. Xia, H. Yang, J. Gao, D. Zha, M. Zhu, Christina D. Wang*, Zhaoran Wang, and Jian Guo*. Dynamic Datasets and Market Environments for Financial Reinforcement Learning. Machine Learning Journal, Springer Nature, 2023.
  • [TC] X.-Y. Liu, Z. Zhang, Z. Wang, H. Lu, X. Wang, and A. Walid. High-performance tensor learning primitives using GPU tensor cores. IEEE Transactions on Computers, 2022.
  • [TC] H. Huang, X.-Y. Liu*, W. Tong, T. Zhang, A. Walid and X. Wang. High-performance hierarchical Tucker tensor learning using GPU tensor cores. IEEE Transactions on Computers, 2022.
  • [TPDS] T. Zhang, X.-Y. Liu*, and X. Wang. High-Performance GPU tensor completion with tubal-sampling pattern. IEEE Transactions on Parallel and Distributed Systems, 2020.
  • [TPDS] T. Zhang, X.-Y. Liu*, X. Wang, and A. Walid. cuTensor-tubal: Efficient primitives for tubal-rank tensor learning operations on GPUs. IEEE Transactions on Parallel and Distributed Systems, 2019.
  • [TPDS] X.-Y. Liu, Y. Zhu, L. Kong*, C. Liu, Y. Gu, A. V. Vasilakos, M.-Y. Wu. CDC: Compressive data collection for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,Vol.26, No.8, pp. 2188-2197, 2015. (ESI-Highly Cited)
  • [TPDS] L. Kong, M. Xia, X.-Y. Liu*, G. Chen, Y. Gu, M.-Y. Wu, X. Liu. Data loss and reconstruction in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems,Vol. 25, No. 11, pp. 2818-2828, 2014. (ESI-Highly Cited)
  • [TPDS] L. Kong, M. Zhao, X.-Y. Liu, J. Lu, Y. Liu, M.-Y. Wu, and W. Shu. Surface coverage in sensor networks. IEEE Transactions on Parallel and Distributed Systems, Vol. 25, No. 1, pp. 234-243, 2013.
  • [TSP] J. Johnston, X.-Y. Liu, S. Wu, X. Wang. A curriculum learning approach to optimization with application to downlink beamforming. IEEE Transactions on Signal Processing, 2023.
  • [TSP] R. She, P. Fan*, X.-Y. Liu, X. Wang. Interpretable generative adversarial networks with exponential function. IEEE Transactions on Signal Processing, 2021.
  • [ToN] L. Deng, X.-Y. Liu, H. Zheng*, X. Feng, Z. Chen, Graph-tensor neural networks for network traffic data imputation. IEEE Transactions on Networking, 2023.
  • [ToN]  L. Deng, H. Zheng*, X.-Y. Liu, X. Feng, Z. Chen, Network latency estimation with leverage sampling for personal devices: An adaptive tensor completion approach. IEEE Transactions on Networking, 2020.
  • [TMC] X. Liu, K. Zheng, X.-Y. Liu, X. Wang*, and Y. Zhu. Hierarchical cooperation improves delay in cognitive radio networks with heterogeneous mobile secondary nodes. IEEE Transactions on Mobile Computing, Vol. 18, No. 12, pp. 2871-2884, 2018.
  • [TMC] X. Liu, K. Zheng, L. Fu, X.-Y. Liu, X. Wang*, and G. Dai. Energy efficiency of secure cognitive radio networks with cooperative spectrum sharing. IEEE Transactions on Mobile Computing, Vol. 18, No. 2, pp. 305-318, 2018.
  • [TMC] X.-Y. Liu, S. Aeron, V. Aggarwal, X. Wang*, M.-Y. Wu. Adaptive sampling of RF fingerprints for fine-grained indoor localization. IEEE Transactions on Mobile Computing, 2016.
  • [TITS] L. Deng, X.-Y. Liu, H. Zheng*, X. Feng, Y. Chen. Graph spectral regularized tensor completion for traffic data imputation. IEEE Transactions on Intelligent Transportation Systems, 2021.
  • [TITS] M. Zhu, X.-Y. Liu, X. Wang*. Joint transportation and charging scheduling in public vehicle systems-a game theoretic approach. IEEE Transactions on Intelligent Transportation Systems, 2018.

  • [TITS] M. Zhu, X.-Y. Liu*, X. Wang. An online ride-sharing path planning strategy for public vehicle systems. IEEE Transactions on Intelligent Transportation Systems, 2018.

  • [TITS] M. Zhu, X.-Y. Liu, M. Qiu, W. Shu, F. Tang, R. Shen, M.-Y. Wu. Public vehicles for future urban transportation systems. IEEE Transactions on Intelligent Transportation Systems, 2016.
  • [TSE] J. Yang, C. Fu*, X.-Y. Liu, H. Yin, P. Zhou. Codee: A tensor embedding scheme for binary code search. IEEE Transactions on Software Engineering, 2021.
  • [TDSC] C. Fu, X.-Y. Liu*, J. Yang, L. T. Yang, S. Yu, and T. Zhu. Wormhole: The hidden virus propagation power of a search engine in social networks. IEEE Transactions on Dependable and Secure Computing, 2017.
  • [TII] L. Kong, X.-Y. Liu, H. Sheng, P. Zeng, and G. Chen. Federated tensor mining for secure industrial Internet of Things. IEEE Transactions on Industrial Informatics, 2019.
  • [TBD] X.-Y. Liu, X. Wang*. LS-decomposition for robust recovery of sensory big data. IEEE Transactions on Big Data, 2017.
  • [TALLIP] X.-Y. Liu, Y. Zhang, Y. Liao, and L. Jiang*. Dynamic updating of the knowledge base for a large-scale question answering system. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Vol. 19, no. 3, pp. 1-13, 2020.
  • [COMNET] X.-Y. Liu, K.-L. Wu, Y. Zhu, L. Kong, M.-Y. Wu. Mobility increases the surface coverage of distributed sensor networks. Elsevier Computer Networks, 2013.
  • [Book Chapter 9] X.-Y. Liu, Y. Fang, L. Yang, Z. Li, A. Walid. High-performance tensor decompositions for compressing and accelerating deep neural networks. Tensors for Data Processing: Theory, Methods, and Applications. [Link] Elsevier; 2021.
  • [Book Chapter 2&7] X.-Y. Liu, Reinforcement learning for cyber-physical systems: with cybersecurity case studies. Chapman & Hall/CRC, 2019.

For manuscripts

TensorLet@gmail.com
Online contact form

Selected Conference Publication

  • [NeurIPS] X.-Y. Liu, Z. Zhang. Classical simulation of quantum circuits using reinforcement learning: parallel environments and benchmark. NeurIPS, Special Track on Datasets and Benchmarks, 2023.
  • [NeurIPS] X.-Y. Liu, Z. Li, X. Wang. Homomorphic matrix completion. NeurIPS, 2022.
  • [NeurIPS] X.-Y. Liu, Z. Xia, J. Rui, J. Gao, H. Yang, M. Zhu, C. Wang, Z. Wang, J. Guo. FinRL-Meta: Market environments and benchmarks for data-driven financial reinforcement learning. NeurIPS, Special Track on Datasets and Benchmarks, 2022.
  • [AAAI] D. Liu, S. Xu, X.-Y. Liu, Z. Xu, W. Wei, P. Zhou. Spatiotemporal graph neural network based mask reconstruction for video object segmentation. AAAI, 2021.
  • [AAAI] X. Han, B. Wu, Z. Shou, X.-Y. Liu*, Y. Zhang, L. Kong. Tensor FISTA-Net for real-time snapshot compressive imaging. AAAI, 2020.
  • [AAAI] F. Jiang, X.-Y. Liu*, H. Lu, R. Shen. Efficient multi-dimensional tensor sparse coding using t-linear combinations. AAAI, 2018. [Slides] and [Poster]
  • [CVPR] M. Yin, S. Liao, X.-Y. Liu, X. Wang, and B. Yuan. Towards extremely compact RNNs for video recognition with fully decomposed hierarchical Tucker structure. CVPR 2021.
  • [ICCV] J. Ma, X.-Y. Liu*, Z. Shou, X. Yuan. Deep tensor ADMM-net for snapshot compressive imaging. ICCV, 2019. [paper].
  • [IJCAI] H. Zhang, Q. Yan, P. Zhou, X.-Y. Liu. Generating robust audio adversarial examples with temporal dependency. IJCAI, 2020.
  • [SIGIR] W. Bao, H. Wen, S. Li, X.-Y. Liu, Q. Lin and K. Yang. GMCM: Graph-based micro-behavior conversion model for post-click conversion rate estimation. SIGIR, Industry Track, 2020. [Slides] and [Video]
  • [WWW] W. Zhang, W. Bao, X.-Y. Liu, K. Yang, Q. Lin, H. Wen, R. Ramezani. Large-scale causal approaches to debiasing post-click conversion rate estimation with multi-task learning. WWW, 2020.
  • [ICAIF] B. Zhang, H. Yang, T. Zhou, A. Babar, Xiao-Yang Liu, Enhancing financial sentiment analysis via retrieval augmented large language models. ACM International Conference on AI in Finance, 2023.
  • [ICAIF Workshop] Berend Jelmer D. Gort, X.-Y. Liu*, J. Gao, Shuaiyu Chen, Christina Dan Wang. Deep reinforcement learning for cryptocurrency trading: Practical approach to address backtest overfitting. ACM International Conference on AI in Finance, Workshop on Benchmarks for AI in Finance, 2022; Also appeared at AAAI’23 Bridge on AI for Financial Services, 2023.
  • [ICAIF] M. Guan, Xiao-Yang Liu*(co-primary). Explainable deep reinforcement learning for portfolio management: An empirical approach. ACM International Conference on AI in Finance,  2021.
  • [ICAIF] Z. Li, Xiao-Yang Liu*(co-primary), J. Zheng, Z. Wang, A. Walid, J. Guo. FinRL-Podracer: High performance and scalable deep reinforcement learning for quantitative finance. ACM International Conference on AI in Finance,  2021.
  • [ICAIF] Xiao-Yang Liu, Hongyang Yang, J. Gao, C. D. Wang. FinRL: Deep reinforcement learning framework to automate trading in quantitative finance. ACM International Conference on AI in Finance,  2021.
  • [ICAIF] Hongyang Yang, Xiao-Yang Liu* (co-primary), S. Zhong, A. Walid, Deep reinforcement learning for automated stock trading: an ensemble strategy. ACM International Conference on AI in Finance,  2020. [PDF]
  • [ICAIF] Q. Chen, Xiao-Yang Liu*, Quantifying ESG alpha using scholar big data: An automated machine learning approach. ACM International Conference on AI in Finance, 2020. [PDF]