Projects (Upgrading)

We optimize GPU tensor library in CUDA environment, providing efficient memory access, batch mode computations and shards mode computations.

We apply deep learning and deep reinforcement learning to IoT tasks. Also, tensor structures are exploited and tensor operations are accelerated.

Sensory big data analysis; Smartphone applications ;RF tomography (VR/AR); Intelligent transportation systems

Big Data in Cloud Computing and SN

We propose secure protocols to support tensor computations on the cloud. Homomophic encrytion
Differential privacy

Algorithm Design Optimization

We design efficient algorithms for big data and deep learning. Our algorithms are robust against massive missing data, measurement noise, and anomaly entries; effective algorithms for tensor time series prediction.

Tensor Decompositions

We study tensor decompositions (CP, Tucker, Tensor-train/Tensor-ring,tensor networks, etc) and propose a transform-based tensor decomposition. We investigate information theoretical results.

Tensors

Welcome to the tensor world!

Selected Publications

(For manuscripts, please drop an email to TensorLet ⊗ gmail.com)

(Corresponding authors are in bold)

[Book] Reinforcement learning for cyber-physical systems. CRC Press . Link.  Editor: Chong Li and Meikang Qiu.  I wrote Chapter 2 and 7.

[TIT] X.-Y. Liu, S. Aeron, V. Aggarwal, X. Wang. Low-tubal-rank tensor completion using alternating minimization. IEEE Transactions on Information Theory, 2019.

[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.

[TBD] X.-Y. Liu, X. Wang. LS-decomposition for robust recovery of sensory big data. IEEE Transactions on Big Data, 2017.

[TPDSX.-Y. Liu, T. Zhang (co-primary author), X. Wang, 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.

[TII] L. Kong, X.-Y. Liu, H. Sheng, P. Zeng, G. Chen. Federated tensor mining for secure industrial Internet of Things. IEEE Transactions on Industrial Informatics, 2019.

[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.  

[TITSM. 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.

[IoT Journal] H. Zheng, M. Gao, Z. Zhang, X.-Y. Liu, X. Feng. An adaptive sampling scheme via approximate volume sampling for fingerprint-based indoor localization. IEEE Internet of Things Journal, 2019.
 
[TCSS] C. Fu, Z. Yang, X.-Y. Liu, A. Walid, L. T. Yang. Secure tensor decomposition for heterogeneous multimedia data in cloud computing. IEEE Transactions on Computational Social Systems, 2019.
 

[JMLR] M. Ashraphijuo, X. Wang. Fundamental conditions for low-CP-rank tensor completion. The Journal of Machine Learning Research18(1), pp.2116-2145, 2017.

[JMLR] M. Ashraphijuo, X. Wang, V. Aggarwal. Rank determination for low-rank data completion. The Journal of Machine Learning Research18(1), pp.3422-3450, 2017

[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.

[TNSE] Y. Wu, X.-Y. Liu, L. Fu, X. Wang. Energy-efficient and robust tensor-encoder for wireless camera networks in Internet of Things. IEEE Transactions on Networks Science and Engineering, 2018.

[Geophysics] F. Qian, M. Yin, X.-Y. Liu, Y.-J. Wang, C. Lu, G.-M. Hu. Unsupervised seismic facies analysis via deep convolutional autoencoders. Geophysics, 2018.

[ICCV] X.-Y. Liu, J. Ma (co-primary author), Z. Shou, X. Yuan. Deep tensor ADMM-net for snapshot compressive imaging. ICCV, 2019.

[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. (alphabetical order[Slides] and [Poster]

[BigData] Y. Fang, X.-Y. Liu, H. Yang. Practical machine learning approach to capture the scholar data driven alpha in AI industry. IEEE Big Data, 5th Special Session on Intelligent Data Mining, 2019.

[IPCCC] J. Huang, L. Kong, X.-Y. Liu, W. Qu and G. Chen. A C++ library for tensor decomposition. International Performance Computing and Communications Conference (IPCCC), 2019.

[ICCAD] C. Deng, M. Yin, X.-Y. Liu, X. Wang, B. Yuan. High-performance hardware architecture for tensor singular value decomposition (Invited paper).  International Conference on Computer-Aided Design (ICCAD), 2019.

[IJCAI WorkshopZ. Ding, X.-Y. Liu, M. Yin, L. Kong, Tensor super-resolution with generative adversarial nets: A large image generation approach. IJCAI Joint Workshop on Human Brain and Artificial Intelligence, 2019.

[KDD Workshop] X. Li, Y. Li, X.-Y. Liu, D. Wang, Risk management via anomaly circumvent: mnemonic deep learning for midterm stock prediction. KDD Workshop on Anomaly Detection in Finance, 2019.

[NeurIPS Workshop] W.  Bao, X.-Y. Liu. Spatial influence-aware reinforcement learning for intelligent transportation system. NeurIPS Workshop on Machine Learning for Autonomous Driving, 2019.

[NeurIPS Workshop] X. Li, Y. Li, H. Yang, L. Yang,  X.-Y. LiuDP-LSTM: A differential privacy framework for stock prediction based on financial news. Robust AI in FS 2019 : NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 2019.

[NeurIPS WorkshopX.-Y. Liu, Z. Ding, S. Borst, A. Walid. Deep reinforcement learning for intelligent transportation systems. NeurIPS Workshop on Machine Learning for Intelligent Transportation Systems, 2018. [PDF] and [codes]. 

[NeurIPS Workshop] Z. Xiong, X.-Y. Liu, S. Zhong, H. Yang, A. Walid. Practical deep reinforcement learning approach for stock trading.  NeurIPS  Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, 2018. [PDF] and [codes].

[NeurIPS Workshop] W. Lu, X.-Y. Liu, Q. Wu, Y. Sun, A. Walid. Transform-based multilinear dynamical system for tensor time series analysis. NeurIPS Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, 2018. (Spotlight Presentation). [PDF] and [codes].

[ICML Workshop] W. Bao, X.-Y. Liu, Multi-agent reinforcement learning for liquidation strategy analysis. ICML Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning, 2019. [Codes]

[ICML Workshop] X. Li, Y. Li, Y. Zhan, X.-Y. Liu. Optimistic Bull or Pessimistic Bear: adaptive deep reinforcement learning for stock portfolio allocation. ICML Workshop on AI in Finance: Applications and Infrastructure for Multi-Agent Learning, 2019. [Codes]

[ICASSPX.-Y. LiuT. Zhang (co-primary author). cuTensor-tubal: Optimized GPU library for low-tubal-rank tensors. IEEE ICASSP, 2019. 

[ICASSP] S. Liao, X.-Y. Liu, F. Qian, G.-M. Hu. Tensor super-resolution for seismic data. IEEE ICASSP, 2019.

[ICASSP] C. Li, Y. Sun, X.-Y. Liu, Y. Li. Tensor subspace detection with tubal-sampling and elementwise-sampling. IEEE ICASSP, 2018. [Codes]

[ICASSP] F. Jiang, X.-Y. Liu, H. Lu, R. Shen. Anisotropic total variation regularized low-rank tensor completion based on tensor nuclear norm for color image inpainting. IEEE ICASSP, 2018.  (alphabetical order)

[ICASSP] X.-Y. Liu, S. Aeron, V. Aggarwal, X. Wang, M.-Y. Wu. Tensor completion via adaptive sampling of tensor fibers: application to efficient indoor RF fingerprinting. IEEE ICASSP, 2016.

[ICME] T. Deng, F. Qian,  X.-Y. Liu, M. Zhang, A. Walid. Tensor sensing for RF tomographic imaging. IEEE ICME, 2018. [Slides] and [Codes]

[ICME] F. Jiang, X.-Y. Liu, H. Lu, R. Shen. Graph regularized tensor sparse coding for image representation. IEEE ICME, 2017.  (alphabetical order)

[HPCC] H. Li, T. Zhang, R. Zhang, X.-Y. Liu. High-performance tensor decoder on GPUs for wireless camera networks in IoT. IEEE HPCC 2019.

[HPCC] H. Lu, T. Zhang, X.-Y. Liu. High-performance homomorphic matrix completion on GPUs. IEEE HPCC 2019.

H. Yang, X.-Y. Liu, Q. Wu. A practical machine learning approach for dynamic stock recommendation. IEEE TrustCom, 2018. [Slides] and [Codes]

H. Zhou, X.-Y. Liu, C. Fu, C. Shang, X. Chang. Differentially private matrix completion via distributed matrix factorization. IEEE TrustCom, 2018.

C. Zhu, L. Xu, X.-Y. Liu, F. Qian. Tensor-generative adversarial network with two-dimensional sparse coding: application to real-time indoor localization. IEEE International Conference on Communications (ICC), 2018. [Slides] and [Data]

[Neurocomputing] F. Jiang, H. Li, X. Hou, B. Sheng, R. Shen, X.-Y. Liu, W. Jia, P. Li, R. Fang: Abdominal adipose tissues extraction using multi-scale deep neural network. Neurocomputing 229: 23-33, 2017.

[ISIT] M. Ashraphijuo, V. Aggarwal, X. Wang. A characterization of sampling patterns for low-Tucker-rank tensor completion problem. IEEE IEEE International Symposium on Information Theory (ISIT), 2017.

[ISIT] M. Ashraphijuo, V. Aggarwal, X. Wang. A characterization of sampling patterns for low-rank multi-view data completion problem. IEEE IEEE International Symposium on Information Theory (ISIT), 2017.

X.-Y. Liu and X. Wang. Fourth-order tensor space with two-dimensional discrete transforms. arXiv,2017

[IWQOS] M. Zhu, X.-Y. Liu, M. Qiu, R. Shen, W. Shu, M.-Y. Wu. Traffic Big Data based Path Planning Strategy in Public Vehicle Systems. IEEE IWQoS, 2016.

Y. Xie, X.-Y. Liu, L. Kong, F. Wu, G. Chen, A. V Vasilakos. Drone-based wireless relay using online tensor update. IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), 2016.

[INFOCOML. Kong, M. Xia, X.-Y. Liu, M.-Y. Wu, Xue Liu. Data loss and reconstruction in sensor networks. IEEE INFOCOM, Turin, Italy, 2013.

[ICDCS] L. Kong, L. He, X.-Y. Liu, Y. Gu, M.-Y. Wu, X. Liu. Privacy-preserving compressive sensing for crowdsensing based trajectory recovery. IEEE ICDCS, Columbus, Ohio, USA, 2015.

* L. Kong, X.-Y. Liu, M. Tao, M.-Y. Wu, Y. Gu, L. Cheng, J. Niu. Resource-efficient data gathering in sensor networks for environment reconstruction. The Computer Journal (COMJ),Vol. 58, No. 6, pp. 1330-1343, 2015.

G. Chen, X.-Y. Liu, L. Kong, J.-L. Lu, M.-Y. Wu. Multi-attribute compressive data gathering. IEEE WCNC, Istanbul, Turkey,2014.

G. Chen, X.-Y. Liu, L. Kong, J.-L. Lu, Y. Gu, W. Shu, M.-Y. Wu. Multiple attributes-based data recovery in wireless sensor networks. IEEE GlobeCom, Atlanda, GA, USA, 2013.

G. Chen, X.-Y. Liu, L. Kong, J.-L. Lu, W. Shu, M.-Y. Wu. JSSDR: Joint-sparse sensory data recovery in wireless sensor networks. IEEE WiMob, Lyon, France, 2013.

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