Hideaki Kim

Learning and Intelligent Systems Research Group
Innovative Communication Laboratory
NTT Communication Science Laboratories


Contact Information:
hideaki.kin (at) ntt.com
2-4 Hikaridai, Seika, Souraku,
Kyoto 619-0237, JAPAN

Research Interests

  • Stochastic processes (point processes, stochastic differential equations, etc.)
  • Kernel methods
  • Machine learning

Publications

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

Peer-reviewed Papers

  1. Yuka Nishida, Hideaki Kim, Takeshi Kurashima,
    Investigation of Optimal Loss-Framed Incentive Strategies Based on a Scenario Experiment to Promote Health Behavior,
    IPSJ Transactions on Databases, vol.18, no.2, pp.1-7, 2025.
  2. Yoshiaki Takimoto, Hideaki Kim, Nozomi Kobayashi, Takeshi Kurashima,
    Evaluation of Incentives for Achieving Effective Health Promotion Messages,
    IEICE Transactions on Information and Systems, vol.108-D, no.5, 2025.
  3. Susumu Fujii, Sayuri Nonaka, Hideaki Kim, Taichi Asami, Shinichi Egawa,
    Awareness Survey on Provision of Medical Information in Time of Disaster,
    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. Hideaki Kim, Tomoharu Iwata, Hiroshi Sawada,
    A New Probabilistic Topic Model Based on Variable Bin Width Histogram,
    Technical Report of IEICE, vol.116, no.121, pp.217-223, 2016.
  2. Hideaki Kim, Noriko Takaya, Hiroshi Sawada,
    Discovering Temporal Dynamics of Consumer's Purchase Decisions based on Hierarchical Time-Rescaling Model,
    Technical Report of IEICE, 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

  • Thesis
    Estimating Synaptic Inputs from Neuronal Spike Signals based on Neuronal Models

    Division of Physics and Astronomy, Graduate School of Science, Kyoto University
    Supervisor: Prof. Shigeru Shinomoto
    Ph.D. (Science), March 2013

Prize

  • NeurIPS2024 Top Reviewer Award, 2024.
  • IEICE TC-IBISML Research Award Finalist, 2017,
    "A New Probabilistic Topic Model Based on Variable Bin Width Histogram".
  • JNNS Conference Encouragement Award, 2012,
    "Estimating Nonstationary Inputs from Firing Rate and Non-Poisson Irregularity in a Single Spike Train".
  • JNNS Best Paper Award, 2010,
    "Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex", PLoS Computational Biology 5(7): e1000433, 2009.