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Liu Song  柳 松

ORCIDConnect your ORCID iD *help
Researcher Number 80760579
Affiliation (based on the past Project Information) *help 2016 – 2017: 統計数理研究所, 統計的機械学習研究センター, 特任助教
2015: 統計数理研究所, その他部局等, 研究員
Review Section/Research Field
Principal Investigator
Statistical science
Except Principal Investigator
Complex systems
Keywords
Principal Investigator
Supervised Learning / Posterior Ratio / 人工知能 / 統計数学 / Machine Learning / Artificial Intelligence / Graphical Model / Markov Network / Transfer Learning / Density Ratio Estimation … More
Except Principal Investigator
… More アルゴリズム / 最適化 / ベイズ推論 / セミパラメトリック / スパースモデリング Less
  • Research Projects

    (2 results)
  • Research Products

    (19 results)
  • Co-Researchers

    (4 People)
  •  Onsite Transfer LearningPrincipal Investigator

    • Principal Investigator
      Liu Song
    • Project Period (FY)
      2015 – 2016
    • Research Category
      Grant-in-Aid for Research Activity Start-up
    • Research Field
      Statistical science
    • Research Institution
      The Institute of Statistical Mathematics
  •  Deepening and applications of sparse modeling by approaches of semiparametric Bayesian inference

    • Principal Investigator
      Fukumizu Kenji
    • Project Period (FY)
      2013 – 2017
    • Research Category
      Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
    • Review Section
      Complex systems
    • Research Institution
      The Institute of Statistical Mathematics

All 2017 2016 2015 2014

All Journal Article Presentation

  • [Journal Article] Trimmed Density Ratio Estimation2017

    • Author(s)
      Liu, S., Takeda, A., Suzuki, T. and Fukumizu, K.
    • Journal Title

      Advances in Neural Information Processing Systems (NIPS 2017)

      Volume: 30

    • Peer Reviewed / Open Access
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Journal Article] Learning sparse structural changes in high-dimensional Markov networks2017

    • Author(s)
      Song Liu, Kenji Fukumizu, and Taiji Suzuki
    • Journal Title

      Behaviormetrika

      Volume: 44(1) Issue: 1 Pages: 265-286

    • DOI

      10.1007/s41237-017-0014-z

    • Peer Reviewed / Acknowledgement Compliant / Open Access / Int'l Joint Research
    • Data Source
      KAKENHI-PLANNED-25120012, KAKENHI-PROJECT-15H06823, KAKENHI-PROJECT-25730013
  • [Journal Article] Structure Learning of Partitioned Markov Networks2016

    • Author(s)
      Liu, S. Suzuki, T., Sugiyama, M. Fukumizu K.
    • Journal Title

      In the Proceedings of International Conference on Machine Learning 2016

      Volume: なし

    • Peer Reviewed / Acknowledgement Compliant / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Journal Article] Support Consistency of Direct Sparse-Change Learning in Markov Networks2016

    • Author(s)
      Song Liu, Suzuki Taiji, Raissa Relator, Jun Sese, Masashi Sugiyama, and Kenji Fukumizu
    • Journal Title

      Annals of Statistics

      Volume: 2016 Pages: 34-34

    • NAID

      110009971454

    • Peer Reviewed / Acknowledgement Compliant
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Journal Article] Structure Learning of Partitioned Markov Networks2016

    • Author(s)
      Song Liu, Taiji Suzuki, Masashi Sugiyama, and Kenji Fukumizu
    • Journal Title

      Proc. 33rd International Conference on Machine Learning

      Volume: 1 Pages: 1-9

    • Peer Reviewed / Acknowledgement Compliant / Open Access
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Journal Article] Structure Learning of Partitioned Markov Networks2016

    • Author(s)
      Song Liu, Taiji Suzuki , Masashi Sugiyama, Kenji Fukumizu
    • Journal Title

      Proceedings of The 33rd International Conference on Machine Learning

      Volume: - Pages: 439-448

    • Peer Reviewed / Acknowledgement Compliant / Open Access
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Journal Article] Support consistency of direct sparse-change learning in Markov networks.2016

    • Author(s)
      Liu, S., Suzuki, T., Relator R., Sese J., Sugiyama, M., Fukumizu, K.
    • Journal Title

      Annals of Statistics

      Volume: なし

    • Peer Reviewed / Acknowledgement Compliant / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Journal Article] Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective2016

    • Author(s)
      Liu, S., Fukumizu K.
    • Journal Title

      In the Proceedings of SIAM International Conference on Data Minin, 2016

      Volume: なし

    • Peer Reviewed / Acknowledgement Compliant / Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Journal Article] Support Consistency of Direct Sparse-Change Learning in Markov Networks2014

    • Author(s)
      Song Liu, Taiji Suzuki, and Masashi Sugiyama
    • Journal Title

      The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI2015)

      Volume: 1 Pages: 2785-2791

    • NAID

      110009971454

    • Peer Reviewed / Open Access
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Presentation] Developments on Learning Changes between Graphical Models2017

    • Author(s)
      Song Liu
    • Organizer
      2017 Probabilistic Graphical Model Workshop: Structure, Sparsity and High-dimensionality
    • Place of Presentation
      東京
    • Year and Date
      2017-02-22
    • Int'l Joint Research
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Presentation] Recent Developments on Learning Changes between Graphical Models2017

    • Author(s)
      Song Liu
    • Organizer
      2017 Probabilistic Graphical Model Workshop at ISM
    • Place of Presentation
      The Institute of Statistical Mathematics
    • Invited
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Presentation] Structure learning of partitioned Markov networks2016

    • Author(s)
      Song Liu
    • Organizer
      ERATO感謝祭, National Institute of Informatics
    • Place of Presentation
      National Institute of Informatics, Tokyo
    • Invited
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Presentation] Structure learning of partitioned Markov networks2016

    • Author(s)
      Song Liu
    • Organizer
      MIRU2016-The 19th Meeting on Image Recognition and Understanding
    • Place of Presentation
      Hamamatsu
    • Invited
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Presentation] Structure Learning of Partitioned Markov Networks2016

    • Author(s)
      Song Liu, Taiji Suzuki, Masashi Sugiyama, and Kenji Fukumizu
    • Organizer
      33rd International Conference on Machine Learning
    • Place of Presentation
      New York
    • Year and Date
      2016-06-19
    • Int'l Joint Research
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Presentation] Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective2016

    • Author(s)
      Song Liu
    • Organizer
      SIAM Internatioanal Conference on Data Mining
    • Place of Presentation
      Hilton Downtoan, Miami, US
    • Year and Date
      2016-05-07
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Presentation] Structure Learning of Partitioned Markov Networks2016

    • Author(s)
      Song Liu
    • Organizer
      International Conference on Machine Learning
    • Place of Presentation
      Marriott Marquis hotel, NYC, US
    • Year and Date
      2016-06-18
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-15H06823
  • [Presentation] Partitioned Markov Networks2016

    • Author(s)
      Song Liu, Taiji Suzuki , Masashi Sugiyama, Kenji Fukumizu
    • Organizer
      MIRU2016 第19回画像の認識・理解シンポジウム
    • Place of Presentation
      浜松
    • Invited
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Presentation] Structure Learning of Partitioned Markov Networks2016

    • Author(s)
      Song Liu, Taiji Suzuki , Masashi Sugiyama, Kenji Fukumizu
    • Organizer
      The 33rd International Conference on Machine Learning
    • Place of Presentation
      New York
    • Year and Date
      2016-06-20
    • Int'l Joint Research
    • Data Source
      KAKENHI-PLANNED-25120012
  • [Presentation] Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective2015

    • Author(s)
      Song Liu
    • Organizer
      NIPS workshop: Transfer and Multi-Task Learning: Trends and New Perspectives
    • Place of Presentation
      Palais des Congress de Montreal, Canada
    • Year and Date
      2015-12-12
    • Int'l Joint Research
    • Data Source
      KAKENHI-PROJECT-15H06823
  • 1.  NISHIYAMA Yu (60586395)
    # of Collaborated Projects: 2 results
    # of Collaborated Products: 0 results
  • 2.  Fukumizu Kenji (60311362)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 8 results
  • 3.  鈴木 大慈 (60551372)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 9 results
  • 4.  冨岡 亮太 (70518282)
    # of Collaborated Projects: 1 results
    # of Collaborated Products: 0 results

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