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Distinguished Researcher Hideaki Kim
  • Distinguished Researcher

    Hideaki Kim

  • NTT Communication Science Laboratories

Prediction and Control of Real-World Phenomena based on Statistical Machine Learning

We aim to establish machine learning techniques for accurately predicting when and where diverse real-world phenomena occur, and for steering them toward desirable outcomes.

In this page

Awards

  • NeurIPS2024 Top Reviewer Award, 2024
  • IEEE MDM Honorable Mention Award, 2017
  • IEICE TC-IBISML Research Award Finalist, 2017
  • JNNS Conference Encouragement Award, 2012
  • JNNS Best Paper Award, 2010

Academic Activities

  • Editorial Board Member, IPSJ Transactions on Mathematical Modeling and Its Applications (Apr. 2026-)

Visiting Professor

  • Part-Time Lecture, Yokohama City University (Sep. 2024-)

Publications

Papers

  • Hideaki Kim, Tomoharu Iwata, "A Representer Theorem for Hawkes Processes via Penalized Least Squares Minimization," International Conference on Learning Representations (ICLR), oral, 2026.
  • Hideaki Kim, Tomoharu Iwata, Akinori Fujino, "K2IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes," International Conference on Machine Learning (ICML), 2025.
  • Hideaki Kim, "Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions," Neural Information Processing Systems (NeurIPS), 2024.
  • Hideaki Kim, "Survival Permanental Processes for Survival Analysis with Time-Varying Covariates," Neural Information Processing Systems (NeurIPS), 2023.
  • Hideaki Kim, Taichi Asami, Hiroyuki Toda, "Fast Bayesian Estimation of Point Process Intensity as Function of Covariates," Neural Information Processing Systems (NeurIPS), 2022.

Keywords

Machine learning, Event sequence analysis, Point processes, Kernel methods, Stochastic differential equations

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