Liu Song 柳松

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 Density Ratio Estimation / Transfer Learning / Markov Network / Graphical Model / Artificial Intelligence / Machine Learning / 統計数学 / 人工知能 / Posterior Ratio / Supervised Learning … More Except Principal Investigator … More スパースモデリング / セミパラメトリック / ベイズ推論 / 最適化 / アルゴリズム Less

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 Networks: A Review on Methodologies and Theories2017
- Author(s)
  Song Liu, Kenji Fukumizu, and Taiji Suzuki
- Journal Title
  
  Behaviormetrika
  
  Volume: 44(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

Liu Song 柳 松

Research Projects

Research Products

Co-Researchers

Onsite Transfer LearningPrincipal Investigator

Principal Investigator

Project Period (FY)

Research Category

Research Field

Research Institution

Deepening and applications of sparse modeling by approaches of semiparametric Bayesian inference

Principal Investigator

Project Period (FY)

Research Category

Review Section

Research Institution

[Journal Article] Trimmed Density Ratio Estimation2017

Author(s)

Journal Title

Data Source

[Journal Article] Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories2017

Author(s)

Journal Title

DOI

Data Source

[Journal Article] Structure Learning of Partitioned Markov Networks2016

Author(s)

Journal Title

Data Source

[Journal Article] Support Consistency of Direct Sparse-Change Learning in Markov Networks2016

Author(s)

Journal Title

NAID

Data Source

[Journal Article] Structure Learning of Partitioned Markov Networks2016

Author(s)

Journal Title

Data Source

[Journal Article] Structure Learning of Partitioned Markov Networks2016

Author(s)

Journal Title

Data Source

[Journal Article] Support consistency of direct sparse-change learning in Markov networks.2016

Author(s)

Journal Title

Data Source

[Journal Article] Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective2016

Author(s)

Journal Title

Data Source

[Journal Article] Support Consistency of Direct Sparse-Change Learning in Markov Networks2014

Author(s)

Journal Title

NAID

Data Source

[Presentation] Developments on Learning Changes between Graphical Models2017

Author(s)

Organizer

Place of Presentation

Year and Date

Data Source

[Presentation] Recent Developments on Learning Changes between Graphical Models2017

Author(s)

Organizer

Place of Presentation

Data Source

[Presentation] Structure learning of partitioned Markov networks2016

Author(s)

Organizer

Place of Presentation

Data Source

[Presentation] Structure learning of partitioned Markov networks2016

Author(s)

Organizer

Place of Presentation

Data Source

[Presentation] Structure Learning of Partitioned Markov Networks2016

Author(s)

Organizer

Place of Presentation

Liu Song 柳松