Multiobjective energy management system using modified MOPSO. - Shoichi Kitamura; Kazuyuki Mori; Seiichi Shindo; Yoshio Izui; Yoshihiko Ozaki (SMC, 2005)

Effective hyperparameter optimization using Nelder-Mead method in deep learning. - Yoshihiko Ozaki; Masaki Yano; Masaki Onishi (IPSJ Trans. Comput. Vis. Appl., 2017)

Speeding up of the Nelder-Mead Method by Data-Driven Speculative Execution. - Shuhei Watanabe; Yoshihiko Ozaki; Yoshiaki Bando; Masaki Onishi (ACPR (1), 2019)

Evaluation of Optimization for Pedestrian Route Guidance in Real-world Crowded Scene. - Shusuke Shigenaka; Shunki Takami; Yoshihiko Ozaki; Masaki Onishi; Tomohisa Yamashita; Itsuki Noda (AAMAS, 2019)

Evolutionary Multiobjective Optimization for Pedestrian Route Guidance with Multiple Scenarios. - Yuki Tanigaki; Yoshihiko Ozaki; Shusuke Shigenaka; Masaki Onishi (CEC, 2020)

Multiobjective tree-structured parzen estimator for computationally expensive optimization problems. - Yoshihiko Ozaki; Yuki Tanigaki; Shuhei Watanabe; Masaki Onishi (GECCO, 2020)

Evaluating Initialization of Nelder-Mead Method for Hyperparameter Optimization in Deep Learning. - Shintaro Takenaga; Shuhei Watanabe; Masahiro Nomura; Yoshihiko Ozaki; Masaki Onishi; Hitoshi Habe (ICPR, 2020)

Warm Starting CMA-ES for Hyperparameter Optimization. - Masahiro Nomura; Shuhei Watanabe; Youhei Akimoto; Yoshihiko Ozaki; Masaki Onishi (AAAI, 2021)

MAS-Bench: Parameter Optimization Benchmark for Multi-agent Crowd Simulation. - Shusuke Shigenaka; Shunki Takami; Shuhei Watanabe; Yuki Tanigaki; Yoshihiko Ozaki; Masaki Onishi (AAMAS, 2021)

Global Search versus Local Search in Hyperparameter Optimization. - Yoshihiko Ozaki; Shintaro Takenaga; Masaki Onishi (CEC, 2022)

Modified Nelder-Mead Method for High-Dimensional Low-Budget Optimization. - Shintaro Takenaga; Yoshihiko Ozaki; Masaki Onishi (SSCI, 2022)

Multiobjective Tree-Structured Parzen Estimator. - Yoshihiko Ozaki; Yuki Tanigaki; Shuhei Watanabe; Masahiro Nomura; Masaki Onishi (J. Artif. Intell. Res., 2022)

Dynamic Fidelity Selection for Hyperparameter Optimization. - Shintaro Takenaga; Yoshihiko Ozaki; Masaki Onishi (GECCO Companion, 2023)

Practical initialization of the Nelder-Mead method for computationally expensive optimization problems. - Shintaro Takenaga; Yoshihiko Ozaki; Masaki Onishi (Optim. Lett., 2023)

PLaMo-100B: A Ground-Up Language Model Designed for Japanese Proficiency. - Kenshin Abe; Kaizaburo Chubachi; Yasuhiro Fujita 001; Yuta Hirokawa; Kentaro Imajo; Toshiki Kataoka; Hiroyoshi Komatsu; Hiroaki Mikami; Tsuguo Mogami; Shogo Murai; Kosuke Nakago; Daisuke Nishino; et al. (CoRR, 2024)

Parameter-free dynamic fidelity selection for automated machine learning. - Shintaro Takenaga; Yoshihiko Ozaki; Masaki Onishi (Appl. Soft Comput., 2025)