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Theory and Application of Information-Based Machine Learning

Research Project

Project/Area Number 25700022
Research Category

Grant-in-Aid for Young Scientists (A)

Allocation TypePartial Multi-year Fund
Research Field Intelligent informatics
Research InstitutionThe University of Tokyo (2014-2016)
Tokyo Institute of Technology (2013)

Principal Investigator

Sugiyama Masashi  東京大学, 新領域創成科学研究科, 教授 (90334515)

Project Period (FY) 2013-04-01 – 2017-03-31
Project Status Completed (Fiscal Year 2016)
Budget Amount *help
¥25,350,000 (Direct Cost: ¥19,500,000、Indirect Cost: ¥5,850,000)
Fiscal Year 2016: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2015: ¥7,930,000 (Direct Cost: ¥6,100,000、Indirect Cost: ¥1,830,000)
Fiscal Year 2014: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2013: ¥8,320,000 (Direct Cost: ¥6,400,000、Indirect Cost: ¥1,920,000)
Keywords機械学習 / 情報量 / 密度比 / 密度差 / 密度微分 / 教師付き学習 / 教師なし学習 / 強化学習 / クラス事前確率推定 / 次元削減 / リーマン幾何 / 類似度 / ダイバージェンス / 密度差推定
Outline of Final Research Achievements

In this research project, we developed methods for directly learning the density ratio and density difference without estimating each density, and based on them, we developed various machine learning algorithms. This includes algorithms of semi-supervised classification, unsupervised clustering, supervised causal inference, supervised dimension reduction, unsupervised dimension reduction, classification from positive and unlabeled data, supervised learning under target shift, and cross-domain object matching. We also developed methods for directly learning the density derivative without estimating the density itself, and based on them, we developed algorithms of modal regression and non-Gaussian component analysis.

Report

(5 results)
  • 2016 Annual Research Report   Final Research Report ( PDF )
  • 2015 Annual Research Report
  • 2014 Annual Research Report
  • 2013 Annual Research Report
  • Research Products

    (46 results)

All 2017 2016 2015 2014 2013 Other

All Int'l Joint Research (3 results) Journal Article (21 results) (of which Int'l Joint Research: 2 results,  Peer Reviewed: 20 results,  Acknowledgement Compliant: 16 results) Presentation (15 results) (of which Int'l Joint Research: 10 results) Book (3 results) Remarks (4 results)

  • [Int'l Joint Research] Universite Paris-Dauphine(フランス)

    • Related Report
      2016 Annual Research Report
  • [Int'l Joint Research] Seoul National University(韓国)

    • Related Report
      2016 Annual Research Report
  • [Int'l Joint Research] Technischen Universitaet Darmstadt(ドイツ)

    • Related Report
      2016 Annual Research Report
  • [Journal Article] Geometry-aware principal component analysis for symmetric positive definite matrices2017

    • Author(s)
      Horev, I., Yger, F., & Sugiyama, M.
    • Journal Title

      Machine Learning

      Volume: -

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Int'l Joint Research / Acknowledgement Compliant
  • [Journal Article] Class-prior estimation for learning from positive and unlabeled data2017

    • Author(s)
      du Plessis, M. C., Niu, G., & Sugiyama, M.
    • Journal Title

      Machine Learning

      Volume: -

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Model-based reinforcement learning with dimension reduction2016

    • Author(s)
      Tangkaratt, V., Morimoto, J., & Sugiyama, M.
    • Journal Title

      Neural Networks

      Volume: 84 Pages: 1-16

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Direct density-derivative estimation2016

    • Author(s)
      Sasaki, H., Noh, Y.-K., Niu, G., & Sugiyama, M.
    • Journal Title

      Neural Computation

      Volume: 28 Pages: 1101-1140

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Int'l Joint Research / Acknowledgement Compliant
  • [Journal Article] Regularized multi-task learning for multi-dimensional log-density gradient estimation2016

    • Author(s)
      Yamane, I., Sasaki, H., & Sugiyama, M.
    • Journal Title

      Neural Computation

      Volume: 28 Pages: 1388-1410

    • NAID

      110009971442

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Computationally efficient class-prior estimation under class balance change using energy distance2016

    • Author(s)
      Kawakubo, H., du Plessis, M. C., & Sugiyama, M.
    • Journal Title

      IEICE Transactions on Information and Systems

      Volume: E99-D Pages: 176-186

    • NAID

      130005116200

    • Related Report
      2015 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Bandit-based task assignment for heterogeneous crowdsourcing2015

    • Author(s)
      Zhang, H., Ma, Y., & Sugiyama, M.
    • Journal Title

      Neural Computation

      Volume: 27 Pages: 2447-2475

    • Related Report
      2015 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] 相互情報量を用いた機械学習とそのロボティクスへの応用2015

    • Author(s)
      杉山 将, 入江 清, 友納 正裕
    • Journal Title

      日本ロボット学会誌

      Volume: 33 Pages: 86-91

    • NAID

      130005065137

    • Related Report
      2015 Annual Research Report
    • Acknowledgement Compliant
  • [Journal Article] Cross-domain matching with squared-loss mutual information2015

    • Author(s)
      Yamada, M., Sigal, L., Raptis, M., Toyoda, M., Chang, Y., & Sugiyama, M.
    • Journal Title

      IEEE Transactions on Pattern Analysis and Machine Intelligence

      Volume: 37 Pages: 1764-1776

    • Related Report
      2015 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Online direct density-ratio estimation applied to inlier-based outlier detection2015

    • Author(s)
      du Plessis, M. C., Shiino, H., & Sugiyama, M.
    • Journal Title

      Neural Computation

      Volume: 27 Pages: 1899-1914

    • Related Report
      2015 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Semi-supervised information-maximization clustering.2014

    • Author(s)
      Calandriello, D., Niu, G., & Sugiyama, M.
    • Journal Title

      Neural Networks

      Volume: 57 Pages: 103-111

    • Related Report
      2014 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Class prior estimation from positive and unlabeled data.2014

    • Author(s)
      du Plessis, M. C. & Sugiyama, M.
    • Journal Title

      IEICE Transactions on Information and Systems

      Volume: E97-D Pages: 1358-1362

    • NAID

      130004519253

    • Related Report
      2014 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Constrained least-squares density-difference estimation.2014

    • Author(s)
      Nguyen, T. D., du Plessis, M. C., Kanamori, T., & Sugiyama, M.
    • Journal Title

      IEICE Transactions on Information and Systems

      Volume: E97-D Pages: 1822-1829

    • NAID

      130004519278

    • Related Report
      2014 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Least-squares independence regression for non-linear causal inference under non-Gaussian noise.2014

    • Author(s)
      Yamada, M., Sugiyama, M., & Sese, J.
    • Journal Title

      Machine Learning

      Volume: 96 Pages: 249-267

    • Related Report
      2014 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Unsupervised dimension reduction via least-squares quadratic mutual information.2014

    • Author(s)
      Sainui, J. & Sugiyama, M.
    • Journal Title

      IEICE Transactions on Information and Systems

      Volume: E97-D Pages: 2806-2809

    • NAID

      110009971453

    • Related Report
      2014 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] 非定常環境下での学習:共変量シフト適応,クラスバランス変化適応,変化検知.2014

    • Author(s)
      杉山 将, 山田 誠, ドゥ・プレシ マーティヌス・クリストフェル, リウ ソン.
    • Journal Title

      日本統計学会論文誌

      Volume: 44 Pages: 113-136

    • NAID

      110009864639

    • Related Report
      2014 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Journal Article] Statistical analysis of distance estimators with density differences and density ratios2014

    • Author(s)
      Kanamori, T. & Sugiyama, M.
    • Journal Title

      Entropy

      Volume: 16 Pages: 921-942

    • Related Report
      2013 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Semi-supervised learning of class balance under class-prior change by distribution matching2014

    • Author(s)
      du Plessis, M. C. & Sugiyama, M.
    • Journal Title

      Neural Networks

      Volume: 50 Pages: 110-119

    • NAID

      110009545983

    • Related Report
      2013 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Information-maximization clustering based on squared-loss mutual information2014

    • Author(s)
      Sugiyama, M., Niu, G., Yamada, M., Kimura, M., & Hachiya, H.
    • Journal Title

      Neural Computation

      Volume: 26 Pages: 84-131

    • Related Report
      2013 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Direct approximation of quadratic mutual information and its application to dependence-maximization clustering.2013

    • Author(s)
      Sainui, J. & Sugiyama, M.
    • Journal Title

      IEICE Transactions on Information and Systems

      Volume: E96-D Pages: 2282-2285

    • NAID

      130004519212

    • Related Report
      2013 Annual Research Report
    • Peer Reviewed
  • [Journal Article] Learning under non-stationarity: Covariate shift and class-balance change.2013

    • Author(s)
      Sugiyama, M., Yamada, M., & du Plessis, M. C.
    • Journal Title

      WIREs Computational Statistics

      Volume: - Pages: 1-13

    • Related Report
      2013 Annual Research Report
    • Peer Reviewed
  • [Presentation] Least-squares log-density gradient clustering for Riemannian manifolds2017

    • Author(s)
      Ashizawa, M., Sasaki, H., Sakai, T., & Sugiyama, M.
    • Organizer
      29th International Conference on Artificial Intelligence and Statistics (AISTATS2017)
    • Place of Presentation
      Fort Lauderdale, Florida, USA
    • Year and Date
      2017-04-20
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Policy search with high-dimensional context variables2017

    • Author(s)
      Tangkaratt, V., van Hoof, H., Parisi, S., Neumann, G., Peters, J., & Sugiyama, M.
    • Organizer
      Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI2017)
    • Place of Presentation
      San Francisco, California, USA
    • Year and Date
      2017-02-04
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Semi-supervised sufficient dimension reduction under class-prior change2016

    • Author(s)
      Kawakubo, H. & Sugiyama, M.
    • Organizer
      Conference on Technologies and Applications of Artificial Intelligence (TAAI2016)
    • Place of Presentation
      Hsinchu, Taiwan
    • Year and Date
      2016-11-25
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Geometry-aware stationary subspace analysis2016

    • Author(s)
      Horev, I., Yger, F., & Sugiyama, M.
    • Organizer
      8th Asian Conference on Machine Learning (ACML2016)
    • Place of Presentation
      Hamilton, New Zealand
    • Year and Date
      2016-11-16
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Modal regression via direct log-density derivative estimation2016

    • Author(s)
      Sasaki, H., Ono, Y., & Sugiyama, M.
    • Organizer
      23rd International Conference on Neural Information Processing (ICONIP2016)
    • Place of Presentation
      Kyoto, Japan
    • Year and Date
      2016-10-16
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Non-Gaussian component analysis with log-density gradient estimation2016

    • Author(s)
      Sasaki, H., Niu, G., & Sugiyama, M.
    • Organizer
      19th International Conference on Artificial Intelligence and Statistics (AISTATS2016)
    • Place of Presentation
      Cadiz, Spain
    • Year and Date
      2016-05-09
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Geometry-aware principal component analysis for symmetric positive definite matrices2015

    • Author(s)
      Horev, I., Yger, F., & Sugiyama, M.
    • Organizer
      Asian Conference on Machine Learning (ACML2015)
    • Place of Presentation
      Hong Kong, China
    • Year and Date
      2015-11-20
    • Related Report
      2015 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Sufficient dimension reduction via direct estimation of the gradients of logarithmic conditional densities2015

    • Author(s)
      Sasaki, H., Tangkaratt, V., & Sugiyama, M.
    • Organizer
      Asian Conference on Machine Learning (ACML2015)
    • Place of Presentation
      Hong Kong, China
    • Year and Date
      2015-11-20
    • Related Report
      2015 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Class-prior estimation for learning from positive and unlabeled data2015

    • Author(s)
      du Plessis, M. C., Niu, G., & Sugiyama, M.
    • Organizer
      Asian Conference on Machine Learning (ACML2015)
    • Place of Presentation
      Hong Kong, China
    • Year and Date
      2015-11-20
    • Related Report
      2015 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Target shift adaptation in supervised learning2015

    • Author(s)
      Nguyen, T. D., du Plessis, M. C., & Sugiyama, M.
    • Organizer
      Asian Conference on Machine Learning (ACML2015)
    • Place of Presentation
      Hong Kong, China
    • Year and Date
      2015-11-20
    • Related Report
      2015 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Clustering via mode seeking by direct estimation of the gradient of a log-density.2014

    • Author(s)
      Sasaki, H., Hyvarinen, A., & Sugiyama, M.
    • Organizer
      European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD2014)
    • Place of Presentation
      Nancy, France
    • Year and Date
      2014-09-15 – 2014-09-19
    • Related Report
      2014 Annual Research Report
  • [Presentation] Transductive learning with multi-class volume approximation.2014

    • Author(s)
      Niu, G., Dai, B., du Plessis, M. C., & Sugiyama, M.
    • Organizer
      International Conference on Machine Learning (ICML2014)
    • Place of Presentation
      Beijing, China,
    • Year and Date
      2014-06-21 – 2014-06-26
    • Related Report
      2014 Annual Research Report
  • [Presentation] Bias reduction and metric learning for nearest-neighbor estimation of Kullback-Leibler divergence.2014

    • Author(s)
      Noh, Y.-K., Sugiyama, M., Liu, S., du Plessis, M. C., Park, F. C., & Lee, D. D.
    • Organizer
      International Conference on Artificial Intelligence and Statistics (AISTATS2014)
    • Place of Presentation
      Reykjavik, Iceland
    • Year and Date
      2014-04-22 – 2014-04-24
    • Related Report
      2014 Annual Research Report
  • [Presentation] Squared-loss mutual information regularization.2013

    • Author(s)
      Niu, G., Jitkrittum, W., Dai, B., Hachiya, H., & Sugiyama, M.
    • Organizer
      30th International Conference on Machine Learning (ICML2013)
    • Place of Presentation
      Atlanta, Georgia, USA
    • Related Report
      2013 Annual Research Report
  • [Presentation] Clustering unclustered data: Unsupervised binary labeling of two datasets having different class balances.2013

    • Author(s)
      du Plessis, M. C., Niu, G., & Sugiyama, M.
    • Organizer
      Conference on Technologies and Applications of Artificial Intelligence (TAAI2013),
    • Place of Presentation
      Taipei, Taiwan
    • Related Report
      2013 Annual Research Report
  • [Book] Introduction to Statistical Machine Learning2015

    • Author(s)
      Sugiyama, M.
    • Total Pages
      534
    • Publisher
      Morgan Kaufmann
    • Related Report
      2015 Annual Research Report
  • [Book] 異常検知と変化検知2015

    • Author(s)
      井手 剛, 杉山 将
    • Total Pages
      192
    • Publisher
      講談社
    • Related Report
      2015 Annual Research Report
  • [Book] イラストで学ぶ機械学習:最小二乗法による識別モデル学習を中心に2013

    • Author(s)
      杉山 将
    • Total Pages
      230
    • Publisher
      講談社
    • Related Report
      2013 Annual Research Report
  • [Remarks] 杉山将のホームページ

    • URL

      http://www.ms.k.u-tokyo.ac.jp/sugi/

    • Related Report
      2016 Annual Research Report
  • [Remarks] Masashi Sugiyama's Web Page

    • URL

      http://www.ms.k.u-tokyo.ac.jp/sugi/

    • Related Report
      2015 Annual Research Report
  • [Remarks] http://www.ms.k.u-tokyo.ac.jp

    • Related Report
      2014 Annual Research Report
  • [Remarks] 杉山将のページ

    • URL

      http://sugiyama-www.cs.titech.ac.jp/~sugi/

    • Related Report
      2013 Annual Research Report

URL: 

Published: 2013-05-21   Modified: 2019-07-29  

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