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Research and development of nonlinear Selective Inference for high-dimensional and small number of samples data

Research Project

Project/Area Number 20H04243
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Review Section Basic Section 61030:Intelligent informatics-related
Research InstitutionOkinawa Institute of Science and Technology Graduate University (2023)
Kyoto University (2020-2022)

Principal Investigator

Yamada Makoto  沖縄科学技術大学院大学, 機械学習とデータ科学ユニット, 准教授 (00581323)

Co-Investigator(Kenkyū-buntansha) 下平 英寿  京都大学, 情報学研究科, 教授 (00290867)
POIGNARD BENJAMIN  大阪大学, 大学院経済学研究科, 准教授 (40845252)
Project Period (FY) 2020-04-01 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
Budget Amount *help
¥17,680,000 (Direct Cost: ¥13,600,000、Indirect Cost: ¥4,080,000)
Fiscal Year 2023: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2022: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2021: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥5,200,000 (Direct Cost: ¥4,000,000、Indirect Cost: ¥1,200,000)
Keywords選択的推論 / 特徴選択 / 電子透かし / カーネル法 / 木構造最適輸送距離 / 統計的推論 / 機械学習
Outline of Research at the Start

バイオロジーや医療分野においては, 特徴数 (例:遺伝子数) が標本数 (例:患者数) よりも大きい高次元 小標本データの解析が重要テーマの一つである. 本研究課題では, 選択的推論 (Selective Inference)と 呼ばれる特徴選択と統計的仮説検定を組み合わせた方法の研究開発を実施する. 具体的には, 研究代 表者らが独自に研究を進めているカーネル法に基づいた非線形選択的推論の枠組みを高次元小標本 データを扱えるように拡張することを目指す. さらに, 提案アルゴリズムを急性骨髄性白血病やアト ピー性皮膚炎といった現実の問題に適用しその有効性を示すことを目的とする.

Outline of Final Research Achievements

In this research, we worked on a high-dimensional extension of nonlinear selective inference. In FY2020, we developed a statistical hypothesis testing method using HSIC Lasso and the Split method, and demonstrated its effectiveness on real data. In FY2021, we proposed a method based on HSIC with Polyhedral Lemma and Knockoff filter, which were reported in ICML 2021 and AISTATS 2022, respectively. In the fiscal year 2022, we proposed a new high-dimensional data analysis method based on the optimal transport method, which was presented at AISTATS 2022 and TMLR, respectively. In the final year, we proposed the Distance Covariance Lasso method and showed the theoretical properties of selective inference.

Academic Significance and Societal Importance of the Research Achievements

本研究は、非線形選択的推論を高次元データに適用する新たな手法を提案し, 統計的仮説検定の検出力向上を目指した.  さらに, 木構造最適輸送に基づくBarycenterの推定手法やWasserstein距離の学習方法など, 新たな高次元データ解析手法を開発した. つまり, 我々は非線形データの特徴選択とスクリーニングの理論的基盤を確立したと言える. さらに今後, 機械学習やバイオインフォマティクス分野での実用的な応用され, 提案法による新規の科学的発見が期待できる. これらの成果は、学術的意義に加え、社会的にも広範な影響を与えると考える.

Report

(5 results)
  • 2023 Annual Research Report   Final Research Report ( PDF )
  • 2022 Annual Research Report
  • 2021 Annual Research Report
  • 2020 Annual Research Report
  • Research Products

    (22 results)

All 2023 2022 2021 Other

All Int'l Joint Research (5 results) Journal Article (13 results) (of which Int'l Joint Research: 7 results,  Peer Reviewed: 13 results,  Open Access: 13 results) Presentation (4 results) (of which Int'l Joint Research: 4 results)

  • [Int'l Joint Research] University of Cambridge(英国)

    • Related Report
      2023 Annual Research Report
  • [Int'l Joint Research] Zhejiang University(中国)

    • Related Report
      2023 Annual Research Report
  • [Int'l Joint Research] University of Technology Sydney(オーストラリア)

    • Related Report
      2021 Annual Research Report
  • [Int'l Joint Research] Carnegie Mellon University/Facebook AI Research/University of Texas Austin(米国)

    • Related Report
      2021 Annual Research Report
  • [Int'l Joint Research] University of Cambridge(英国)

    • Related Report
      2021 Annual Research Report
  • [Journal Article] High-Dimensional Nonlinear Feature Selection with Hilbert-Schmidt Independence Criterion Lasso2023

    • Author(s)
      山田 誠, Poignard Benjamin, 山田 宏暁, Freidling Tobias
    • Journal Title

      Journal of the Japan Statistical Society, Japanese Issue

      Volume: 53 Issue: 1 Pages: 49-67

    • DOI

      10.11329/jjssj.53.49

    • ISSN
      0389-5602, 2189-1478
    • Year and Date
      2023-09-07
    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Nystrom Method for Accurate and Scalable Implicit Differentiation2023

    • Author(s)
      Ryuichiro Hataya, Makoto Yamada
    • Journal Title

      International Conference on Artificial Intelligence and Statistics

      Volume: 206 Pages: 4643-4654

    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Large-scale similarity search with Optimal Transport2023

    • Author(s)
      Clea Laouar, Yuki Takezawa, Makoto Yamada
    • Journal Title

      Empirical Methods in Natural Language Processing (EMNLP)

      Volume: n/a Pages: 11920-11930

    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Robust Graph Dictionary Learning2023

    • Author(s)
      Weijie Liu, Jiahao Xie, Chao Zhang, Makoto Yamada, Nenggan Zheng, Hui Qian
    • Journal Title

      International Conference on Learning Representations

      Volume: n/a

    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Approximating 1-Wasserstein Distance with Trees.2022

    • Author(s)
      Makoto Yamada, Yuki Takezawa, Ryoma Sato, Han Bao, Zornitsa Kozareva, Sujith Ravi
    • Journal Title

      Transactions on Machine Learning Research

      Volume: 0 Pages: 0-0

    • Related Report
      2022 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Fixed Support Tree-Sliced Wasserstein Barycenter2022

    • Author(s)
      Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada
    • Journal Title

      International Conference on Artificial Intelligence and Statistics (AISTATS)

      Volume: -

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Feature Screening with Kernel Knockoff2022

    • Author(s)
      Benjamin Poignard, Peter Naylor, Hector Climente, Makoto Yamada
    • Journal Title

      International Conference on Artificial Intelligence and Statistics (AISTATS)

      Volume: -

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Post-selection inference with HSIC-Lasso2021

    • Author(s)
      Tobias Freidling, Benjamin Poignard, Hector Climente-Gonzalez, Makoto Yamada
    • Journal Title

      International Conference on Machine Learning (ICML)

      Volume: - Pages: 3439-3448

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Supervised Tree-Wasserstein Distance2021

    • Author(s)
      Yuki Takezawa, Ryoma Sato, Makoto Yamada
    • Journal Title

      International Conference on Machine Learning (ICML)

      Volume: - Pages: 10086-10095

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] LSMI-Sinkhorn: Semi-supervised Mutual Information Estimation with Optimal Transport2021

    • Author(s)
      Yanbin Liu, Makoto Yamada, Yao-Hung Hubert Tsai, Tam Le, Ruslan Salakhutdinov, Yi Yang
    • Journal Title

      European Conference on Machine Learning (ECML)

      Volume: - Pages: 655-670

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Random Features Strengthen Graph Neural Networks2021

    • Author(s)
      Ryoma Sato, Makoto Yamada, Hisashi Kashima
    • Journal Title

      SIAM data mining (SDM)

      Volume: - Pages: 333-341

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares2021

    • Author(s)
      Hiroaki Yamada, Makoto Yamada
    • Journal Title

      NeurIPS

      Volume: -

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Flow-based Alignment Approaches for Probability Measures in Different Spaces2021

    • Author(s)
      Tam Le, Nhat Ho, Makoto Yamada
    • Journal Title

      International Conference on Artificial Intelligence and Statistics (AISTATS)

      Volume: - Pages: 3934-3942

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Presentation] Feature screening with kernel knockoffs2022

    • Author(s)
      Benjamin Poignard, Peter J. Naylor, Hector Climente-Gonzalez, Makoto Yamada
    • Organizer
      AISTATS 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Fixed Support Tree-Sliced Wasserstein Barycenter2022

    • Author(s)
      Yuki Takezawa, Ryoma Sato, Zornitsa Kozareva, Sujith Ravi, Makoto Yamada
    • Organizer
      AISTATS 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Re-evaluating Word Mover’s Distance2022

    • Author(s)
      Ryoma Sato, Makoto Yamada, Hisashi Kashima
    • Organizer
      ICML 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Feature-Robust Optimal Transport for High-Dimensional Data.2022

    • Author(s)
      Mathis Petrovich, Chao Liang, Ryoma Sato, Yanbin Liu, Yao-Hung Hubert Tsai, Linchao Zhu, Yi Yang, Ruslan Salakhutdinov, Makoto Yamada
    • Organizer
      ECML 2022
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research

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Published: 2020-04-28   Modified: 2025-01-30  

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