| 2023 | AISTATS | Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. | Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger |
| 2023 | ICLR | Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. | Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama |
| 2021 | ACML | Skew-symmetrically perturbed gradient flow for convex optimization. | Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Ikko Yamane |
| 2021 | ICML | Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. | Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama |
| 2020 | ACML | A One-step Approach to Covariate Shift Adaptation. | Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama |
| 2020 | ICML | Do We Need Zero Training Loss After Achieving Zero Training Error? | Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama |
| 2016 | ACML | Multitask Principal Component Analysis. | Ikko Yamane, Florian Yger, Maxime Berar, Masashi Sugiyama |