Nozomu Yoshinari — accepted AI-conference papers

2025

Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers

Rin Ashizawa, Yoichi Hirose, Nozomu Yoshinari, Kento Uchida, Shinichi Shirakawa

ACL 2025finding

Prompt optimization aims to search for effective prompts that enhance the performance of large language models (LLMs). Although existing prompt optimization methods have discovered effective prompts, they often differ from sophisticated prompts carefully designed by human experts. Prompt design stra…

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2019

Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search

Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida

ICML 2019oral

High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them out-of-the-box to their own problems, albeit its purpose is to automate a part of tuning process. Aiming at a fast, robu…

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