A Fourier-Analytic Approach to List-Decoding for Sparse Random Linear Codes. - Akinori Kawachi; Ikko Yamane (IEICE Trans. Inf. Syst., 2015)

Multitask Principal Component Analysis. - Ikko Yamane; Florian Yger; Maxime Berar; Masashi Sugiyama (ACML, 2016)

Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation. - Ikko Yamane; Hiroaki Sasaki; Masashi Sugiyama (Neural Comput., 2016)

Uplift Modeling from Separate Labels. - Ikko Yamane; Florian Yger; Jamal Atif; Masashi Sugiyama (NeurIPS, 2018)

Do We Need Zero Training Loss After Achieving Zero Training Error? - Takashi Ishida 001; Ikko Yamane; Tomoya Sakai 001; Gang Niu 001; Masashi Sugiyama (ICML, 2020)

Skew-symmetrically perturbed gradient flow for convex optimization. - Futoshi Futami; Tomoharu Iwata; Naonori Ueda; Ikko Yamane (ACML, 2021)

Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. - Ikko Yamane; Junya Honda; Florian Yger; Masashi Sugiyama (ICML, 2021)

A One-Step Approach to Covariate Shift Adaptation. - Tianyi Zhang; Ikko Yamane; Nan Lu; Masashi Sugiyama (SN Comput. Sci., 2021)

Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. - Ikko Yamane; Yann Chevaleyre; Takashi Ishida 001; Florian Yger (AISTATS, 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 001; Ikko Yamane; Nontawat Charoenphakdee; Gang Niu 001; Masashi Sugiyama (ICLR, 2023)

Scalable and hyper-parameter-free non-parametric covariate shift adaptation with conditional sampling. - François Portier; Lionel Truquet; Ikko Yamane (CoRR, 2023)

How Can I Publish My LLM Benchmark Without Giving the True Answers Away? - Takashi Ishida 001; Thanawat Lodkaew; Ikko Yamane (CoRR, 2025)