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日野 英逸

ALUMNI 2026年10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. Orlicz–Sobolev with Musielak: An Efficient Regularization Approach for Graph-based IPM Tam Le; Truyen Nguyen; sity of Akron; Hideitsu HinoKenji Fukumizu
      The Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS 2026) (2026)
  2. 2. 黒潮流軸の類似検索による漂流・漂着軽石ハザードの即時可視化システム 桑谷 立; 北尾 馨; 日野 英逸; 赤穂 昭太郎西川 悠
      Geoinformatics (2026)
  3. 3. Enhancing Certified Robustness in Few-Shot Classification with Contrastive Loss and Defensive Noise in Fine-Tuning Hiroya Kato; Seira Hidano; Takao Murakami; Hideitsu Hino
      Conference: 12th International Conference on Information Systems Security and Privacy (ICISSP2026) (2026)
  4. 4. Evidence for Missing Geomagnetic Reversals From Geomagnetic Reversal Frequency Model Using Adaptive Kernel Density Estimation Yutaka Yoshimura; Masakazu Fujii; Hideitsu Hino; Shotaro Akaho (+6 著者) Yo-ichiro Otofuji
      Geophysical Research Letters (2026) Semantic Scholar
  5. 5. Domain Adaptation with Additional Features via Label-Aware and Graph-Based Fused Gromov--Wasserstein Optimal Transport Toshimitsu Aritake; Hideitsu Hino
      Neural Computation (2026)
  6. 6. Tree Structure for the Categorical Wasserstein Weisfeiler-Lehman Graph Kernel Keishi Sando; Tam Le; Hideitsu Hino
      Transactions on Machine Learning Research (2025)
  7. 7. An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph authors Hideaki Ishibashi; Kota Matsui; Kentaro Kutsukake; Hideitsu Hino
      Lecture Notes in Computer Science 52 (2025)
  8. 8. 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)
  9. 9. 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)
  10. 10. Misspecifying Non-Compensatory as Compensatory IRT: Analysis of Estimated Skills and Variance Hiroshi Tamano; Hideitsu Hino; Daichi Mochihashi
      Behaviormetrika (2025)
  11. 11. 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)
  12. 12. Complex non-backtracking matrix for directed graphs Keishi Sando; Hideitsu Hino
      Journal of Complex Networks 13: (2025)
  13. 13. Dynamics of the accelerated t-SNE Kyoichi Iwasaki; Hideitsu Hino
      Transactions on Machine Learning Research (2025)
  14. 14. 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)
  15. 15. 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)
  16. 16. 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)
  17. 17. 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)
  18. 18. Difference-of-submodular Bregman Divergence Takahiro Kawashima; Masanari Kimura; Tasuku Soma; Hideitsu Hino
      The 13-th International Conference on Learning Representations (ICLR2025) (2025)
  19. 19. An embedding structure of determinantal point process Hideitsu Hino; Keisuke Yano
      Information Geometry (2024) Semantic Scholar
  20. 20. 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)
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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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