Qibin Zhao

Unit leader at Tensor Learning Unit (TLU), RIKEN Center for Advanced Intelligence Project (AIP).
Email: qibin.zhao [at] riken.jp

Tensor Learning Unit

We study various tensor-based machine learning technologies, e.g., tensor decomposition, multilinear latent variable model, tensor regression and classification, tensor networks, deep tensor learning, and Bayesian tensor learning, with aim to facilitate the learning from high-order structured data or large-scale latent space. Our goal is to develop innovative, scalable and efficient tensor learning algorithms supported by theoretical principles. The novel applications in computer vision and brain data analysis will also be explored to provide new insights into tensor learning methods.


  • 01.2020   Prof. Michael Ng (The University of Hong Kong, China) give a talk "Robust Tensor Completion and its Application".
  • 11.2019   Qibin Zhao gave a talk at CAS-RIKEN young scientist seminar.
  • 11.2019   Our three papers (one oral and two poster) have been accepted to AAAI 2020.
  • 10.2019   Our paper has been accepted to ACML 2019 (Journal Track).
  • 09.2019   2 papers have been accepted to NeurIPS 2019.
  • 08.2019   Prof. Baogang Hu (Chinese Academy of Sciences, China) give a talk "Genealized constraints for knowledge-driven-and-data-driven approaches".
  • 08.2019   Prof. Liqing Zhang (Shanghai Jiao Tong University, China) give a talk "Image Disentangled Representation and Multi-Attribute Transfer".
  • 05.2019   Our paper has been accepted to ACL 2019.
  • 05.2019   Our paper received IEEE Signal Processing Magazine Best Paper Award. [Paper]
  • 05.2019   Longhao Yuan recieved the Best Student Paper Award. [Paper]
  • 02.2019   2 papers have been accepted to CVPR 2019.
  • 02.2019   5 papers have been accepted to ICASSP 2019.
  • More

  • 11.2018   Our paper has been accepted to AAAI 2019.
  • 10.2018   Unit Leader Qibin Zhao talked at International Workshop on Tensor Networks and Machine Learning, Skoltech, Moscow.
  • 06.2018   Dr. Guillaume Rabusseau (McGill University, Canada) give a talk "Machine Learning with Tensors for Structured Data".
  • 06.2018   Lecture at Waseda University. [Slides]
  • 05.2018   Prof. Danilo Mandic (Imperial College London, UK) give a talk "Hearables: Enabling technologies for lifelong learning in E-Health".
  • 04.2018   Our paper has been accepted to IJCAI 2018.
  • 03.2018   Prof. Justin Dauwels (Nanyang Technological University, Singapore) give a talk "Bayesian Inference of Sparse Networks (BISN)".
  • 01.2018   Our paper has been accepted to ICLR 2018 (Workshop).
  • 01.2018   Our paper has been accepted to ICASSP 2018.
  • 12.2017   Dr. Fabien Lotte (Inria Bordeaux Sud-Ouest, France) give a talk "Combining machine learning and psychology to design usable Brain-Computer Interfaces".
  • 12.2017   Dr. Tomasz M. RUTKOWSKI (Cogent Labs Inc., The University of Tokyo, Japan) give a talk "End-to-end Deep-learning Approaches for Online BCI and Offline Experiment Brainwave Analyses".


Unit Leader

Qibin Zhao

Postdoctoral Researcher

Ming Hou           Chao Li             Ning Zheng


Xuyang Zhao


Visiting Scientist

Andrzej CichockiToshihisa TanakaJianting CAOCesar F. CaiafaYOKOTA Tatsuya.


Michael Ng (The University of Hong Kong),  Qiang Wu (Shandong University),
Baogang Hu (Chinese Academy of Sciences),  Liqing Zhang (Shanghai Jiao Tong University),
Jordi Solé-Casals (University of Vic–Central University of Catalonia),
Brahim CHAIB-DRAA (Laval University),  Tomasz M. RUTKOWSKI (The University of Tokyo),
Fabien Lotte (Inria Bordeaux Sud-Ouest),  Justin Dauwels (Nanyang Technological University),
Danilo Mandic (Imperial College London),  Guillaume Rabusseau (Mila / Université de Montréal).

Former members

Zihao Huang, Binghua Li (Nankai University, Intern),
Jianfu Zhang (Shanghai Jiao Tong University, Intern),
Zerui Tao (Lanzhou University, Intern),
Andong Wang (Nanjing University of Science and Technology, Intern),
Ziyao Wang (Southeast University, Intern),
Longhao Yuan (Saitama Institute of Technology, Parttimer),
Jiajia Tang (Hangzhou Dianzi University, Intern),
Anjie Zhang (University of Electro-Communications, Parttimer),
Xingwei Cao (McGill University, Parttimer),
Jianqiang Li, Yuyuan Yu, Jinshi Yu, Shifeng Huang, Xinqi Chen (Guangdong University of Technology, Intern).

Selected Publications

Full publication list [PDF]

  • Beyond unfolding: Exact recovery of latent convex tensor decomposition under reshuffling
    C. Li, M. E. Khan, Z. Sun, G. Niu, B. Han, S. Xie, and Q. Zhao
    [C] AAAI 2020
    [Paper] [Poster] [Code]

  • Robust tensor decomposition via orientation invariant tubal nuclear norms
    A. Wang, C. Li, Z. Jin, and Q. Zhao
    [C] AAAI 2020
    [Paper] [Material] [Slide] [Code]

  • Deep multimodal multilinear fusion with high-order polynomial pooling
    M. Hou, J. Tang, J. Zhang, W. Kong, Q. Zhao
    [C] NeurIPS 2019
    [Paper] [Poster] [Code]

  • Dynamic pet image reconstruction using nonnegative matrix factorization incorporated with deep image prior
    T. Yokota, K. Kawai, M. Sakata, Y. Kimura, H. Hontani
    [C] ICCV 2019

  • Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
    P. Liang, Z. Liu, Y. Tsai, Q. Zhao, R. Salakhutdinov, L. Morency
    [C] ACL 2019

  • Guaranteed Matrix Completion Under Multiple Linear Transformations
    C. Li, W. He, L. Yuan, Z. Sun, Q. Zhao
    [C] CVPR 2019
    [Paper] [Slide]

  • Non-Local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
    W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao
    [C] CVPR 2019

  • Learning Efficient Tensor Representations with Ring-structured Networks
    Q. Zhao, M. Sugiyama, L. Yuan, A, Cichocki
    [C] ICASSP 2019
    [Paper] [Poster]

  • Low-rank Embedding of Kernels in Convolutional Neural Networks under Random Shuffling
    C. Li, Z. Sun, J. Yu, M. Hou, Q. Zhao
    [C] ICASSP 2019
    [Paper] [Poster]

  • Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction
    L. Yuan, C. Li, J. Cao, Q. Zhao
    [C] ICASSP 2019
    [Paper] [Poster]

  • Total-variation-regularized Tensor Ring Completion for Remote Sensing Image Reconstruction
    W. He, L. Yuan, N. Yokoya
    [C] ICASSP 2019
    [Paper] [Poster]

  • Tensor ring decomposition with rank minimization on latent space: An efficient approach for tensor completion
    L. Yuan, C. Li, M. Danilo, J. Cao, Q. Zhao
    [C] AAAI 2019

  • Remote sensing image reconstruction using tensor ring completion and total variation
    W. He, N. Yokoya, L. Yuan, Q. Zhao
    [J] IEEE Transactions on Geoscience and Remote Sensing

  • Tensorizing generative adversarial nets
    X. Cao, X. Zhao, Q. Zhao
    [C] ICCE Asia 2018 (International Conference On Consumer Electronics)
    [Paper] [Code]

  • Generative adversarial positive-unlabeled learning
    M. Hou, B. Chaib-draa, C. Li, Q. Zhao
    [C] IJCAI 2018
    [Paper] [Slide] [Code]

  • AI neurotechnology for aging societies–task-load and dementia EEG digital biomarker development using information geometry machine learning methods
    T. M. Rutkowski, Q. Zhao, M. S. Abe, M. Otake
    [C] NeurIPS Workshop 2018

  • High-order tensor completion for data recovery via sparse tensor-train optimization
    L. Yuan, Q. Zhao, J. Cao
    [C] ICASSP 2018

  • Detection of epileptic foci based on interictal iEEG by using convolutional neural network
    X. Zhao, Q. Zhao, T. Tanaka, J. Cao, W. Kong, H. Sugano, N. Yoshida
    [C] DSP 2018 (International Conference on Digital Signal Processing)

  • Feature extraction for incomplete data via low-rank tensor decomposition with feature regularization
    Q. Shi, Y. Cheung, Q. Zhao, H. Lu
    [J] IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

  • High-order tensor completion via gradient-based optimization under tensor train format
    L. Yuan, Q. Zhao, L. Gui, J. Cao
    [J] Signal Processing: Image Communication

  • Feature extraction for incomplete data via low-rank Tucker decomposition
    Q. Shi, Y. Cheung, and Q. Zhao
    [C] ECML PKDD 2017 (Joint European Conference on Machine Learning and Knowledge Discovery in Databases)

  • Tensor Ring Decomposition
    Q. Zhao, G. Zhou, S. Xie, L. Zhang, A. Cichocki
    [J] [arXiv]


  • Tensor networks for dimensionality reduction and large-scale optimization: Part 2 applications and future perspectives
    A. Cichocki, A. Phan, Q. Zhao, N. Lee, I. Oseledets, M. Sugiyama, D. P. Mandic et al
    Foundations and Trends in Machine Learning

  • Tensor networks for dimensionality reduction and large-scale optimization: Part 1 low-rank tensor decompositions
    A. Cichocki, N. Lee, I. Oseledets, A. Phan, Q. Zhao, D. P. Mandic et al
    Foundations and Trends in Machine Learning


  • TRLRF: Tensor Ring Low-rank Factors (Matlab) [Github]
  • TGAN: Tensorizing Generative Adversarial Nets (Python) [Github]
  • Gan PU: Generative Adversarial Positive-Unlabeled Learning (Python) [Code]
  • FBCP: Bayesian CP Factorization for Tensor Completion (Matlab) [Github]
  • BRTF: Bayesian Robust Tensor Factorization (Matlab) [Github]
  • T3C: Tensor-Train Tensor Completion (Matlab) [Github]


  • Tensor Network Representation for Machine Learning - Recent Advances and Perspectives [Slide]
  • Waseda University Lectures
  • Lecture 1: Multilinear Algebra and Tensor Decompositions [Slide]
    Lecture 2: Tensor Networks [Slide]
    Lecture 3: Tensor Methods for Signal Processing and Machine Learning [Slide]


Access to Center for Advanced Intelligence Project

RIKEN Center for Advanced Intelligence Project,
Nihonbashi 1-chome Mitsui Building, 15th floor,
1-4-1 Nihonbashi, Chuo-ku, Tokyo.
〒103-0027, Japan.
Tel: +81-(0)48-467-3626

Job Opportunities

Seeking Openings for Research Scientists or Postdoctoral Researchers or Technical Scientist or Internship Student. [Job]

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