Yamane, Ikko

9 publications

JMLR 2024 Nearest Neighbor Sampling for Covariate Shift Adaptation François Portier, Lionel Truquet, Ikko Yamane

ICLR 2023 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

AISTATS 2023 Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger

ICML 2021 Mediated Uncoupled Learning: Learning Functions Without Direct Input-Output Correspondences Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama

ACML 2021 Skew-Symmetrically Perturbed Gradient Flow for Convex Optimization Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Ikko Yamane

ACML 2020 A One-Step Approach to Covariate Shift Adaptation Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama

ICML 2020 Do We Need Zero Training Loss After Achieving Zero Training Error? Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama

NeurIPS 2018 Uplift Modeling from Separate Labels Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama

ACML 2016 Multitask Principal Component Analysis Ikko Yamane, Florian Yger, Maxime Berar, Masashi Sugiyama