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日野 英逸

ALUMNI 2026年3月現在、筑波大学が主たる所属機関ではありません

Hino, Hideitsu

統計数理研究所 , 教授
The Institute of Statistical Mathematics , Professor

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  1. 1. An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph authors Tam Le; Truyen Nguyen; Hideitsu Hino; Kenji Fukumizu
      The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025) (2025)
  2. 2. An (epsilon,delta)-accurate level set estimation with a stopping criterion Hideaki Ishibashi; Kota Matsui; Kentaro Kutsukake; Hideitsu Hino
      European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2025 (ECML/PKDD2025) (2025)
  3. 3. Information-based probabilistic verification scores and predictability measures: Seasonal prediction examples Yuhei Takaya; Hideitsu Hino; Caio A.; S. Coelho
      Quarterly Journal of the Royal Meteorological Society (2025)
  4. 4. Misspecifying Non-Compensatory as Compensatory IRT: Analysis of Estimated Skills and Variance Hiroshi Tamano; Hideitsu Hino; Daichi Mochihashi
      Behaviormetrika (2025)
  5. 5. Scalable Sobolev IPM for Probability Measures on a Graph Tam Le; Truyen Nguyen; Hideitsu Hino; Kenji Fukumizu
      Forty-Second International Conference on Machine Learning (ICML2025) (2025)
  6. 6. Complex non-backtracking matrix for directed graphs Keishi Sando; Hideitsu Hino
      Journal of Complex Networks 13: (2025)
  7. 7. Dynamics of the accelerated t-SNE Kyoichi Iwasaki; Hideitsu Hino
      Transactions on Machine Learning Research (2025)
  8. 8. Sparse Coding-Based Multiframe Superresolution for Efficient Synchrotron Radiation Microspectroscopy Yasuhiko Igarashi; Naoka Nagamura; Masahiro Sekine; Hirokazu Fukidome (+1 著者) Masato Okada
      Discover Nano (2025)
  9. 9. Optimal Spectroscopic Measurement Design: Bayesian Framework for Rational Data Acquisition Yusei Ito; Yasuo Takeichi; Hideitsu Hino; Kanta Ono
      Machine Learning: Science and Technology 6: 025037 (2025)
  10. 10. A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependence Takahiro Kawashima; Hideitsu Hino
      The 28th International Conference on Artificial Intelligence and Statistics (AISTATS2025) (2025)
  11. 11. Data-driven proactive prediction of pumice drifting patterns using similarity search of the Kuroshio current axis Tatsu Kuwatani; Hideitsu Hino; Haruka Nishikawa; Shotaro Akaho
      npj Natural Hazards 2: (2025)
  12. 12. Difference-of-submodular Bregman Divergence Takahiro Kawashima; Masanari Kimura; Tasuku Soma; Hideitsu Hino
      The 13-th International Conference on Learning Representations (ICLR2025) (2025)
  13. 13. An embedding structure of determinantal point process Hideitsu Hino; Keisuke Yano
      Information Geometry (2024) Semantic Scholar
  14. 14. When Should We Use Top Coding in Locally Private Estimation? Theoretical Bounds of Performance Hajime Ono; Kazuhiro Minami; Hideitsu Hino
      International Journal of Information Security (2024)
  15. 15. Fast symplectic integrator for Nesterov-type acceleration method Shin-itiro Goto; Hideitsu Hino
      Japan Journal of Industrial and Applied Mathematics (2024)
  16. 16. Separating urban heat island circulation and convective cells through dynamic mode decomposition Takuto Sato; Hideitsu Hino; Hiroyuki Kusaka
      Atmospheric Science Letters (2024) Semantic Scholar
  17. 17. Gradual Domain Adaptation via Normalizing Flows Shogo Sagawa; Hideitsu Hino
      Neural Computation (2024)
  18. 18. Rational partitioning of spectral feature space for effective clustering of massive spectral image data Yusei Ito; Yasuo Takeichi; Hideitsu Hino; Kanta Ono
      Scientific Reports (2024) Semantic Scholar
  19. 19. Scalable Counterfactual Distribution Estimation in Multivariate Causal Models Thong Pham; Shohei Shimizu; Hideitsu Hino; Tam Le
      3rd Causal Learning and Reasoning (CLeaR 2024) (2024)
  20. 20. A Short Survey on Importance Weighting for Machine Learning Masanari Kimura; Hideitsu Hino
      Transactions on Machine Learning Research (2024) Semantic Scholar
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  1. 1. 数理科学 2024年4月号 Ｎｏ.730 最適輸送を用いたドメイン適応 ～ 新規変数の観測に対する適応への応用 ～ 日野英逸; 有竹俊光
    株式会社サイエンス社 2024年4月
  2. 2. マテリアル・機械学習・ロボット（現代化学増刊48）， ベイズ最適化の停止基準 日野英逸
    株式会社 東京化学同人 2024年3月
  3. 3. Progress in Information Geometry: Theory and Applications Shin-itiro Goto; Hideitsu Hino (担当:共著)
    Springer Nature 2021年4月
  4. 4. 日本統計学会公式認定 統計検定準1級対応 統計学実践ワークブック 日本統計学会 編 編集委員 竹村彰通(委員長)・青木敏・岩崎学・小林景・中西寛子・原尚幸・日野英逸 他 (担当:分担執筆)
    学術図書出版社 2020年5月
  5. 5. 岩波データサイエンス Vol. 5 Hino,Hideitsu
    2017年2月 (ISBN: 9784000298551)
  6. 6. コンピュー タビジョン 最先端ガイド 6 日野英逸; 村田昇 (担当:分担執筆, 範囲:第3章「スパース表現の数理と応用」)
    アドコム・メディア 2013年12月

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