Search Results for author: Ikko Yamane

Found 6 papers, 4 papers with code

Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences

1 code implementation16 Jul 2021 Ikko Yamane, Junya Honda, Florian Yger, Masashi Sugiyama

In this paper, we consider the task of predicting $Y$ from $X$ when we have no paired data of them, but we have two separate, independent datasets of $X$ and $Y$ each observed with some mediating variable $U$, that is, we have two datasets $S_X = \{(X_i, U_i)\}$ and $S_Y = \{(U'_j, Y'_j)\}$.

A One-step Approach to Covariate Shift Adaptation

no code implementations8 Jul 2020 Tianyi Zhang, Ikko Yamane, Nan Lu, Masashi Sugiyama

A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution.

Do We Need Zero Training Loss After Achieving Zero Training Error?

1 code implementation ICML 2020 Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama

We experimentally show that flooding improves performance and, as a byproduct, induces a double descent curve of the test loss.


Uplift Modeling from Separate Labels

1 code implementation NeurIPS 2018 Ikko Yamane, Florian Yger, Jamal Atif, Masashi Sugiyama

Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments).


Regularized Multi-Task Learning for Multi-Dimensional Log-Density Gradient Estimation

no code implementations1 Aug 2015 Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama

Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring non-Gaussianity.

Density Estimation Multi-Task Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.