Rchr
J-GLOBAL ID:201701015011487155   Update date: Oct. 07, 2024

Hara Satoshi

ハラ サトシ | Hara Satoshi
Affiliation and department:
Homepage URL  (1): https://sites.google.com/site/sato9hara/home
Research field  (1): Intelligent informatics
Research keywords  (5): Machine Learning ,  Explainable AI ,  Feature Selection ,  Anomaly Detection ,  Data Mining
Research theme for competitive and other funds  (8):
  • 2024 - 2029 Desensitization of Algorithms for Decision Making and Knowledge Discovery
  • 2023 - 2028 Active Evaluation of Machine Learning Models
  • 2022 - 2025 Development of data-driven thermal-hydraulic analysis methods using machine learning
  • 2020 - 2024 Personalized Pricing for Time and Space Sharing Services
  • 2020 - 2023 機械学習モデルの説明駆動開発のための基盤技術
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Papers (46):
  • Tomohisa Kumagai, Kazuma Suzuki, Akiyoshi Nomoto, Satoshi Hara, Akiyuki Takahashi. Prediction of the binding energy of self interstitial atoms in alpha iron by a graph neural network. Materialia. 2024. 33. 101977-101977
  • Taichi Tomono, Satoshi Hara, Yusuke Nakai, Kazuma Takahara, Junko Iida, Takashi Washio. A Bayesian approach for constituent estimation in nucleic acid mixture models. Frontiers in Analytical Science. 2024. 3
  • Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Yuta Fujishige, Satoshi Hara. Rule Mining for Correcting Classification Models. 2023 IEEE International Conference on Data Mining (ICDM). 2023. 3. 1331-1336
  • Satoshi Hara, Yuichi Yoshida. Average Sensitivity of Decision Tree Learning. The International Conference on Learning Representations. 2023
  • Gabriel Laberge, Ulrich Aïvodji, Satoshi Hara, Mario Marchand, Foutse Khomh. Fooling SHAP with Stealthily Biased Sampling. The International Conference on Learning Representations. 2023
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MISC (24):
  • 山本雄大, 原聡. スライスワッサースタイン距離を用いた高速なStealthily Biased Sampling. 2024年度人工知能学会全国大会. 2024
  • 寺下直行, 原聡. 低分散で省メモリな勾配推定のためのForward Gradientと誤差逆伝播法の結合. 2024年度人工知能学会全国大会. 2024
  • 清末優子, 五味渕由貴, 安永卓生, 鷲尾隆, 原聡. 細胞の構造的特徴の機械学習による視覚的抽出. 2024年度人工知能学会全国大会. 2024
  • 落合拓真, 瀬野圭一朗, 松井孝太, 原 聡. レベル集合推定に基づく機械学習モデルの能動的評価. 情報論的学習理論と機械学習研究会(IBISML). 2023
  • 波多野 大督, 原 聡, 荒井 ひろみ. 限界貢献を利用した不公平なモデルの修正. 2023年度人工知能学会全国大会. 2023
more...
Books (1):
  • 機械学習工学
    講談社 2022 ISBN:9784065285862
Education (3):
  • 2010 - 2013 Osaka University Graduate School of Engineering Division of Electrical, Electronic and Information Engineering
  • 2008 - 2010 Tokyo Institute of Technology Graduate School of Information Science and Engineering Department of Computer Science
  • 2004 - 2008 Waseda University School of Science and Engineering
Professional career (1):
  • 博士(工学) (大阪大学)
Work history (5):
  • 2024/05 - 現在 The University of Electro-Communications Graduate School of Informatics and Engineering Professor
  • 2020/04 - 2024/04 Osaka University The Institute of Scientific and Industrial Research Associate Professor
  • 2017/09 - 2020/03 Osaka University The Institute of Scientific and Industrial Research Assistant Professor
  • 2016/04 - 2017/08 National Institute of Informatics
  • 2013/04 - 2016/03 IBM Research Tokyo
Awards (3):
  • 2022/11 - 第25回情報論的学習理論ワークショップ(IBIS2022) ベストプレゼンテーション賞 決定木学習の安定化
  • 2019/11 - 第22回情報論的学習理論ワークショップ(IBIS2019) ベストプレゼンテーション賞 SGDの挙動解析に基づくデータクレンジング
  • 2016/07 - 人工知能学会 全国大会優秀賞 機械学習を用いた量子状態異常検知
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