Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex. - Shigeru Shinomoto; Hideaki Kim; Takeaki Shimokawa; Nanae Matsuno; Shintaro Funahashi; Keisetsu Shima; Ichiro Fujita; Hiroshi Tamura; Taijiro Doi; Kenji Kawano; Naoko Inaba; Kikuro Fukushima; et al. (PLoS Comput. Biol., 2009)

Elemental Spiking Neuron Model for Reproducing Diverse Firing Patterns and Predicting Precise Firing Times. - Satoshi Yamauchi; Hideaki Kim; Shigeru Shinomoto (Frontiers Comput. Neurosci., 2011)

Neurons as ideal change-point detectors. - Hideaki Kim; Barry J. Richmond; Shigeru Shinomoto (J. Comput. Neurosci., 2012)

Estimating inputs and an internal neuronal parameter from a single spike train. - Shigeru Shinomoto; Hideaki Kim (EMBC, 2013)

Tracking Temporal Dynamics of Purchase Decisions via Hierarchical Time-Rescaling Model. - Hideaki Kim; Noriko Takaya; Hiroshi Sawada (CIKM, 2014)

Read the Silence: Well-Timed Recommendation via Admixture Marked Point Processes. - Hideaki Kim; Tomoharu Iwata; Yasuhiro Fujiwara; Naonori Ueda (AAAI, 2017)

SVD-Based Screening for the Graphical Lasso. - Yasuhiro Fujiwara; Naoki Marumo; Mathieu Blondel; Koh Takeuchi 001; Hideaki Kim; Tomoharu Iwata; Naonori Ueda (IJCAI, 2017)

Online Traffic Flow Prediction Using Convolved Bilinear Poisson Regression. - Maya Okawa; Hideaki Kim; Hiroyuki Toda (MDM, 2017)

Scaling Locally Linear Embedding. - Yasuhiro Fujiwara; Naoki Marumo; Mathieu Blondel; Koh Takeuchi 001; Hideaki Kim; Tomoharu Iwata; Naonori Ueda (SIGMOD Conference, 2017)

Analyzing Temporal Dynamics of Consumer's Behavior Based on Hierarchical Time-Rescaling. - Hideaki Kim; Noriko Takaya; Hiroshi Sawada (IEICE Trans. Inf. Syst., 2017)

Integrated Optimization of Bipartite Matching and Its Stochastic Behavior: New Formulation and Approximation Algorithm via Min-cost Flow Optimization. - Yuya Hikima; Yasunori Akagi; Hideaki Kim; Masahiro Kohjima; Takeshi Kurashima; Hiroyuki Toda (AAAI, 2021)

Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm. - Yasunori Akagi; Naoki Marumo; Hideaki Kim; Takeshi Kurashima; Hiroyuki Toda (NeurIPS, 2021)

Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation. - Hideaki Kim (NeurIPS, 2021)

Online Matching with Controllable Rewards and Arrival Probabilities. - Yuya Hikima; Yasunori Akagi; Naoki Marumo; Hideaki Kim (IJCAI, 2022)

Fast Bayesian Estimation of Point Process Intensity as Function of Covariates. - Hideaki Kim; Taichi Asami; Hiroyuki Toda (NeurIPS, 2022)

An Improved Approximation Algorithm for Wage Determination and Online Task Allocation in Crowd-Sourcing. - Yuya Hikima; Yasunori Akagi; Hideaki Kim; Taichi Asami (AAAI, 2023)

Survival Permanental Processes for Survival Analysis with Time-Varying Covariates. - Hideaki Kim (NeurIPS, 2023)

MAP inference algorithms without approximation for collective graphical models on path graphs via discrete difference of convex algorithm. - Yasunori Akagi; Naoki Marumo; Hideaki Kim; Takeshi Kurashima; Hiroyuki Toda (Mach. Learn., 2023)

Inverse M-Kernels for Linear Universal Approximators of Non-Negative Functions. - Hideaki Kim (NeurIPS, 2024)

Meta-Learning for Neural Network-based Temporal Point Processes. - Yoshiaki Takimoto; Yusuke Tanaka 002; Tomoharu Iwata; Maya Okawa; Hideaki Kim; Hiroyuki Toda; Takeshi Kurashima (CoRR, 2024)

A Continuous-time Tractable Model for Present-biased Agents. - Yasunori Akagi; Hideaki Kim; Takeshi Kurashima (AAAI, 2025)

K2IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes. - Hideaki Kim; Tomoharu Iwata; Akinori Fujino (CoRR, 2025)