金 秀明（きん ひであき）


NTT株式会社
コミュニケーション科学基礎研究所
協創情報研究部
知能創発環境研究グループ

連絡先:
hideaki.kin (at) ntt.com
〒619-0237 京都府相楽郡精華町光台2-4

Research Interests

  • 確率過程（点過程、確率微分方程式など）
  • カーネル法
  • 機械学習全般

Publications

  • Peer-reviewed Papers
  • Peer-reviewed Conference Papers
  • Reports
  • Preprints

Peer-reviewed Papers

  1. 西田 由佳, 金 秀明, 倉島 健,
    健康行動促進を目的としたシナリオ実験に基づく最適な損失型インセンティブ戦略の検討,
    情報処理学会論文誌データベース, vol.18, no.2, pp.1-7, 2025.
  2. 瀧本 祥章, 金 秀明, 小林 のぞみ, 倉島 健,
    効果的な行動変容メッセージ作成に向けたインセンティブ効果の大規模評価,
    電子情報通信学会論文誌D, vol.108-D, no.5, 2025.
  3. 藤井 進, 野中 小百合, 金 秀明, 浅見 太一, 江川 新一,
    災害時の医療情報提供に関する意識調査,
    Japanese Journal of Disaster Medicine 28(suppl): 454-454, 2024.
  4. Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda,
    MAP Inference Algorithms without Approximation for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm,
    Machine Learning, 112(1): 99-129, 2023.
  5. Hideaki Kim, Noriko Takaya, Hiroshi Sawada,
    Analyzing Temporal Dynamics od Consumer’s Behavior Based on Hierarchical Time-Rescaling Model,
    IEICE Transactions on Information and Systems, E100.D(4): 693-703, 2017.
  6. Hideaki Kim, Shigeru Shinomoto,
    Estimating Nonstationary Inputs from a Single Spike Train based on a Neuron Model with Adaptation,
    Mathematical Biosciences & Engineering, 11(1):49-62, 2014.
  7. Hideaki Kim, Shigeru Shinomoto,
    Estimating Nonstationary Input Signals from a Single Neuronal Spike Train,
    Physical Review E 86:051903, 2012.
  8. Hideaki Kim, Barry J. Richmond, Shigeru Shinomoto,
    Neurons as Ideal Change-point Detectors,
    Journal of Computational Neuroscience 32(1):137-146, 2012.
  9. Satoshi Yamauchi, Hideaki Kim, Shigeru Shinomoto,
    Elemental Spiking Neuron Model for Reproducing Diverse Firing patterns and Predicting Precise Firing Times,
    Frontiers in Computational Neuroscience 5:42, 2011.
  10. Shigeru Shinomoto*, Hideaki Kim*, Toshiaki Shimokawa*, Nanae Matsuno, Shintaro Funahashi, Keisetsu Shima, Ichiro Fujita, Hiroshi Tamura, Taijiro Doi, Kenji Kawano, Naoko Inaba, Kikuro Fukushima, Sergei Kurkin, Kiyoshi Kurata, Masato Taira, Ken-Ichiro Tsutsui, Hidehiko Komatsu, Tadashi Ogawa, Kowa Koida, Jun Tanji, Keisuke Toyama, *: equally contribution,
    Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex,
    PLoS Computational Biology 5(7): e1000433, 2009.

Peer-reviewed Conference Papers

  1. Hideaki Kim, Tomoharu Iwata, Akinori Fujino,
    K2IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes,
    International Conference on Machine Learning (ICML), 2025.
  2. Yasunori Akagi, Hideaki Kim, Takeshi Kurashima,
    A Continuous-time Tractable Model for Present-biased Agents,
    AAAI Conference on Artificial Intelligence (AAAI), oral, 2025.
  3. Hideaki Kim,
    Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions,
    Neural Information Processing Systems (NeurIPS), 2024.
  4. Hideaki Kim,
    Survival Permanental Processes for Survival Analysis with Time-Varying Covariates,
    Neural Information Processing Systems (NeurIPS), 2023.
  5. Yuya Hikima, Yasunori Akagi, Hideaki Kim, Taichi Asami,
    An Improved Approximation Algorithm for Wage Determination and Online Task Allocation in Crowd-sourcing,
    AAAI Conference on Artificial Intelligence (AAAI), 2023.
  6. Hideaki Kim, Taichi Asami, Hiroyuki Toda,
    Fast Bayesian Estimation of Point Process Intensity as Function of Covariates,
    Neural Information Processing Systems (NeurIPS), 2022.
  7. Yuya Hikima, Yasunori Akagi, Naoki Marumo, Hideaki Kim,
    Online Matching with Controllable Rewards and Arrival Probabilities,
    International Joint Conference on Artificial Intelligence (IJCAI), 2022.
  8. Hideaki Kim,
    Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation,
    Neural Information Processing Systems (NeurIPS), spotlight, 2021.
  9. Yasunori Akagi, Naoki Marumo, Hideaki Kim, Takeshi Kurashima, Hiroyuki Toda,
    Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm,
    Neural Information Processing Systems (NeurIPS), 2021.
  10. Yuya Hikima, Yasunori Akagi, Hideaki Kim, Masahiro Kohjima, Takeshi Kurashima, Hiroyuki Toda,
    Integrated Optimization of Bipartite Matching and Its Stochastic Behavior: New Formulation and Approximation Algorithm via Min-cost Flow Optimization,
    AAAI Conference on Artificial Intelligence (AAAI), 2021.
  11. Ryodo Hemmi, Hideaki Miyamoto, H. Inoue, Hiroshi Kikuchi, Hisashi Otake, H. Sato, M. Yamamoto, Y. Hirai, Hideaki Kim,
    High-Precision Digital Terrain Model of a Candidate Landing Site Near Lunar South Pole Using Multi-Image Shape-from-Shading,
    Lunar and Planetary Science Conference, 2020.
  12. Kaisei. Kanetani, Masahiro Yamazaki, Tadatoshi Babasaki, Hideaki Kim, Tatsushi. Matsubayashi,
    Optimization of Maintenance by Failure Prediction Considering Instantaneous and Cumulative Effects of External Environments,
    International Power Electronics Conference (IPEC), 2018.
  13. Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda,
    SVD-Based Screening for the Graphical Lasso,
    International Joint Conference on Artificial Intelligence (IJCAI), 2017.
  14. Yasuhiro Fujiwara, Naoki Marumo, Mathieu Blondel, Koh Takeuchi, Hideaki Kim, Tomoharu Iwata, Naonori Ueda,
    Scaling Locally Linear Embedding,
    ACM International Conference on Management of Data (SIGMOD), 2017.
  15. Maya Okawa, Hideaki Kim, Hiroyuki Toda,
    Online Traffic Flow Prediction using Convolved Bilinear Poisson Regression,
    IEEE International Conference on Mobile Data Management (MDM), 2017.
  16. Hideaki Kim, Tomoharu Iwata, Yasuhiro Fujiwara, Naonori Ueda,
    Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes,
    AAAI Conference on Artificial Intelligence (AAAI), 2017.
  17. Maya Okawa, Aki Hayashi, Hideaki Kim, Takuya Nishimura, Hiroyuki Toda,
    Visualization of Crowd Movements at Large-scale Events,
    IEEE Information Visualization (InfoVis), 2016.
  18. Hideaki Kim, Noriko Takaya, Hiroshi Sawada,
    Tracking Temporal Dynamics of Purchase Decisions via Hierarchical Time-rescaling Model,
    ACM International Conference on Conference on Information and Knowledge Management (CIKM), 2014.
  19. Shigeru Shinomoto, Hideaki Kim,
    Estimating Inputs and an Internal Neuronal parameter from a Single Spike Train,
    Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013.
  20. Hideaki Kim, Barry J. Richmond, Shigeru Shinomoto,
    Estimating Nonstationary Inputs from Firing and Non-Poisson Irregularity in a Single Spike Train,
    Neural Coding, 2012.
  21. Satoshi Yamauchi, Hideaki Kim, Shigeru Shinomoto,
    Firing Patterns Manifested by the Multi-timescale Adaptive Threshold Model,
    Neural Coding, 2010.
  22. Hideaki Kim, Barry J. Richmond, Shigeru Shinomoto,
    Detecting a Change by a Single Neuron,
    Computational and Systems Neuroscience (COSYNE), 2010.
  23. Hideaki Kim, Barry J. Richmond, Shigeru Shinomoto,
    Detecting a Change point by a Single Neuron,
    Neural Coding, 2009.

Reports

  1. 金 秀明, 岩田 具治, 澤田 宏,
    可変ビン幅ヒストグラム密度推定法を組み込んだ確率的トピックモデルの提案,
    電子情報通信学会技術研究報告, vol.116, no.121, pp.217-223, 2016.
  2. 金 秀明, 高屋 典子, 澤田 宏,
    階層的時間伸縮モデルに基づく消費者の購買ダイナミクスの推定,
    電子情報通信学会技術研究報告, vol.115, no.112, pp.9-14, 2015.

Preprints

  1. Hideaki Kim, Takeshi Kurashima,
    Effective Strategy of Financial Incentive for Exercise Adherence,
    Jxiv, 298, 2023.
  2. Hideaki Kim, Hiroshi Sawada,
    Histogram Meets Topic Model: Density Estimation by Mixture of Histograms,
    arXiv, 1512.07960, 2015.

Book

Academic Activity

  • Education Career
  • Prize

Dissertation

  • 博士論文
    神経細胞モデルに基づく神経スパイク信号からのシナプス入力の推定

    京都大学大学院 理学研究科 物理学・宇宙物理学専攻
    指導教員: 篠本 滋 准教授
    博士（理学）, 2013.3

Education Career

  • 横浜市立大学 「DSリテラシー/データ分析基礎」 非常勤講師 (2024.9 - present)

Special Lecture

  • 東北大学経済学研究科 応用統計計量ワークショップ 「点過程理論に基づく購買時刻データの分析」 (2014.10)

Prize

  • NeurIPS2024 Top Reviewer Award, 2024.
  • IBISML研究会賞ファイナリスト, 2017,
    "可変ビン幅ヒストグラム密度推定法を組み込んだ確率的トピックモデルの提案".
  • 日本神経回路学会 大会奨励賞, 2012,
    "神経スパイク列の発火頻度と非ポアソン不規則性から非定常入力信号を推定する".
  • 日本神経回路学会論文賞, 2010,
    "Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex", PLoS Computational Biology 5(7): e1000433, 2009.