Our paper regarding the CMA-ES with Margin for mixed-integer black-box optimization problems has been accepted to ACM Transactions on Evolutionary Learning and Optimization. This paper is joint work with M. Nomura at CyberAgent, Inc.

• Ryoki Hamano, Shota Saito, Masahiro Nomura, and Shinichi Shirakawa: Marginal Probability-Based Integer Handling for CMA-ES Tackling Single-and Multi-Objective Mixed-Integer Black-Box Optimization, ACM Transactions on Evolutionary Learning and Optimization. [DOI] [arXiv]

Our paper regarding the automatic termination for neural architecture search has been accepted to Neural Networks. This work is collaborative research with Prof. Hino (Institute of Statistical Mathematics), etc.

• Kotaro Sakamoto, Hideaki Ishibashi, Rei Sato, Shinichi Shirakawa, Youhei Akimoto, and Hideitsu Hino: ATNAS: Automatic Termination for Neural Architecture Search, Neural Networks, Vol. 166, pp. 446-458, Sep. 2023. [DOI]

Yuta Suzuki, a master’s course student, presented about applying machine learning to neurotoxicity assessment at the International Neurotoxicology Association Conference (INA-18). This work is joint research with National Institute of Health Sciences.

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Our paper has been accepted to Genetic and Evolutionary Computation Conference (GECCO) 2023. This paper introduces the margin correction method into (1+1)-CMA-ES and evaluates it on discrete and mixed-integer black-box optimization problems. This paper is joint work with M. Nomura at CyberAgent, Inc.

Our paper has been accepted to EvoApplications 2023 (Part of Evostar 2023). This work proposes a switching mechanism for target variables of the surrogate model in surrogate-assisted (1+1)-CMA-ES.

• Yutaro Yamada, Kento Uchida, Shota Saito, and Shinichi Shirakawa: Surrogate-Assisted (1+1)-CMA-ES with Switching Mechanism of Utility Functions, EvoApplications 2023 (Held as Part of EvoStar 2023), Brno, Czech Republic, April 12-14, 2023.

Our paper has been accepted to HUCAPP 2023. This work is collaborative research with Prof. Hasegawa at Hokkai Gakuen University and Prof. Kaneko at Aoyama Gakuin University. This paper proposes a method for applying a text-to-gesture generation model trained using an English dataset to Japanese speakers’ data.

Prof. Shirakawa has been selected as a Top Reviewer at NeurIPS 2022. [Link]

Our paper has been accepted to Knowledge-Based Systems. This paper proposes a generation method of EPMA images with desirable physical characteristics based on deep learning. This work is collaborative research with Toyota Motor Corporation.

• Kento Uchida, Genki Sakata, Tetsushi Watari, Yuta Yamakita, and Shinichi Shirakawa: Generation of microscopic structure of solder material with desirable characteristics based on deep learning, Knowledge-Based Systems, Vol. 258, 110017, Dec. 2022. [DOI]

Our paper has been accepted to the Workshop on Edge Learning over 5G Mobile Networks and Beyond in IEEE GLOBECOM 2022). This work is collaborative research with Prof. Nishio at Tokyo Institute of Technology. This paper proposes a neural architecture search method for split computing in which neural networks are split and located at edge devices and servers for inference.