Ikko Yamane - RankMe

18 papers A* 4A 1C 3Journal 10

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	How Can I Publish My LLM Benchmark Without Giving the True Answers Away? CoRR Takashi Ishida, Thanawat Lodkaew, Ikko Yamane
2024	J	`jnl`	Nearest Neighbor Sampling for Covariate Shift Adaptation. J. Mach. Learn. Res. François Portier, Lionel Truquet, Ikko Yamane
2023	A*	`conf`	Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. ICLR Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama
2023	A	`conf`	Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. AISTATS Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger
2023	J	`jnl`	Scalable and hyper-parameter-free non-parametric covariate shift adaptation with conditional sampling. CoRR François Portier, Lionel Truquet, Ikko Yamane
2022	J	`jnl`	Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. CoRR Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama
2021	J	`jnl`	A One-Step Approach to Covariate Shift Adaptation. SN Comput. Sci. Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
2021	A*	`conf`	Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. ICML Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama
2021	J	`jnl`	Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences. CoRR Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama
2021	C	`conf`	Skew-symmetrically perturbed gradient flow for convex optimization. ACML Futoshi Futami, Tomoharu Iwata, Naonori Ueda, Ikko Yamane
2020	C	`conf`	A One-step Approach to Covariate Shift Adaptation. ACML Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
2020	J	`jnl`	A One-step Approach to Covariate Shift Adaptation. CoRR Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama
2020	A*	`conf`	Do We Need Zero Training Loss After Achieving Zero Training Error? ICML Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2020	J	`jnl`	Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama
2018	A*	`conf`	Uplift Modeling from Separate Labels. NeurIPS Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama
2016	C	`conf`	Multitask Principal Component Analysis. ACML Ikko Yamane, Florian Yger, Maxime Berar, Masashi Sugiyama
2016	J	`jnl`	Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation. Neural Comput. Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama
2015	J	`jnl`	A Fourier-Analytic Approach to List-Decoding for Sparse Random Linear Codes. IEICE Trans. Inf. Syst. Akinori Kawachi, Ikko Yamane