A Selective, Just-in-Time Aspect Weaver. - Yoshiki Sato; Shigeru Chiba; Michiaki Tatsubori (GPCE, 2003)

Negligent Class Loaders for Software Evolution. - Yoshiki Sato; Shigeru Chiba (RAM-SE, 2004)

Development of a Visualizing Web Search System. - Norifumi Sunaoka; Setsuo Yokoyama; Yoshiki Sato; Youzou Miyadera (IV, 2004)

Loosely-Separated "Sister" Namespaces in Java. - Yoshiki Sato; Shigeru Chiba (ECOOP, 2005)

Calculation coverage testing in scientific applications. - Yoshiki Sato; Shumpei Hozumi; Shigeru Chiba (ISSTA, 2015)

Development of a Project Based Learning Evaluation Method Based on PBLBOK. - Yoshiki Sato; Atsuo Hazeyama; Shoichi Nakamura; Youzou Miyadera (ICEED, 2018)

Alternating landmark navigation of multiple AUVs for wide seafloor survey: Field experiment and performance verification. - Takumi Matsuda; Toshihiro Maki; Yoshiki Sato; Takashi Sakamaki; Tamaki Ura (J. Field Robotics, 2018)

Docking Method for Hovering-Type AUVs Based on Acoustic and Optical Landmarks. - Toshihiro Maki; Yoshiki Sato; Takumi Matsuda; Kotohiro Masuda; Takashi Sakamaki (J. Robotics Mechatronics, 2018)

Promoting Reflection on Question Decomposition in Web-based Investigative Learning. - Yoshiki Sato; Akihiro Kashihara; Shinobu Hasegawa; Koichi Ota; Ryo Takaoka (ICCE, 2019)

Development of a Training Data Creation Support Environment for Estimating Programming Learning Situations. - Shota Kawaguchi; Tadashi Nishikawa; Yoshiki Sato; Ryo Onuma; Hiroki Nakayama; Shoichi Nakamura; Youzou Miyadera (ITHET, 2019)

Poster: A Tunable Model for Graph Generation Using LSTM and Conditional VAE. - Shohei Nakazawa; Yoshiki Sato; Kenji Nakagawa; Sho Tsugawa; Kohei Watabe (ICDCS, 2021)

A Tunable Model for Graph Generation Using LSTM and Conditional VAE. - Shohei Nakazawa; Yoshiki Sato; Kenji Nakagawa; Sho Tsugawa; Kohei Watabe (CoRR, 2021)

Proposal of a System Enabling Adaptive Support Based on Transition of Each Learner's Source Codes. - Soichiro Sato; Yoshiki Sato; Shoichi Nakamura; Youzou Miyadera (CSCI, 2022)

Spectral Tilt May Have a Smaller Impact on the Intelligibility of Speech in Noise. - Yoshiki Sato; Julián Villegas (ASRU, 2023)

An Accurate Graph Generative Model with Tunable Features. - Takahiro Yokoyama; Yoshiki Sato; Sho Tsugawa; Kohei Watabe (ICCCN, 2023)

Characterization of Locality in Spin States and Forced Moves for Optimizations. - Yoshiki Sato; Makiko Konoshima; Hirotaka Tamura; Jun Ohkubo (CoRR, 2023)

GraphTune: A Learning-Based Graph Generative Model With Tunable Structural Features. - Kohei Watabe; Shohei Nakazawa; Yoshiki Sato; Sho Tsugawa; Kenji Nakagawa (IEEE Trans. Netw. Sci. Eng., 2023)

A Feedback-Enhanced Graph Generative Model With Tunable Features. - Takahiro Yokoyama; Yoshiki Sato; Sho Tsugawa; Kohei Watabe (IEEE Access, 2025)