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] X.-Y. 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, H. Hong, Z. Zhang, W. Tong, J. Kossaifi, X. Wang, and A. Walid. High-performance tensor-train primitives using GPU tensor cores. IEEE Transactions on Computers, 2024.
  • [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] Qianqian Xie, W. Han, Z. Chen, R. Xiang, X. Zhang, Y. He, M. Xiao, D. Li, Y. Dai, D. Feng, Y. Xu, Haoqiang Kang, Z. Kuang, C. Yuan, K. Yang, Z. Luo, T. Zhang, Z. Liu, G. Xiong, Z. Deng, Y. Jiang, Z. Yao, H. Li, Y. Yu, G. Hu, J. Huang, Xiao-Yang Liu, Alejandro Lopez-Lira, B. Wang, Y. Lai, H. Wang, M. Peng, Sophia Ananiadou, Jimin Huang. FinBen: An Holistic Financial Benchmark for Large Language Models. NeurIPS, Special Track on Datasets and Benchmarks, 2024.
  • [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]