| 2025 | ICLR | Flow matching achieves almost minimax optimal convergence. | Kenji Fukumizu, Taiji Suzuki, Noboru Isobe, Kazusato Oko, Masanori Koyama |
| 2024 | ICLR | Neural Fourier Transform: A General Approach to Equivariant Representation Learning. | Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato |
| 2020 | ICML | Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective. | Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro |
| 2020 | SC | Online-Codistillation Meets LARS, Going beyond the Limit of Data Parallelism in Deep Learning. | Shogo Murai, Hiroaki Mikami, Masanori Koyama, Shuji Suzuki, Takuya Akiba |
| 2019 | ICLR | Distributional Concavity Regularization for GANs. | Shoichiro Yamaguchi, Masanori Koyama |
| 2019 | ICML | A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning. | Yoshihiro Nagano, Shoichiro Yamaguchi, Yasuhiro Fujita, Masanori Koyama |
| 2019 | KDD | Optuna: A Next-generation Hyperparameter Optimization Framework. | Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, Masanori Koyama |
| 2018 | ICLR | cGANs with Projection Discriminator. | Takeru Miyato, Masanori Koyama |
| 2018 | ICLR | Spectral Normalization for Generative Adversarial Networks. | Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida |
| 2017 | ICLR | Synthetic Gradient Methods with Virtual Forward-Backward Networks. | Takeru Miyato, Daisuke Okanohara, Shin-ichi Maeda, Masanori Koyama |
| 2015 | PAKDD | Principal Sensitivity Analysis. | Sotetsu Koyamada, Masanori Koyama, Ken Nakae, Shin Ishii |