60 citations · 64 across the 8 of their papers we have counts for
9 papers
CapBencher: Give Your LLM Benchmark a Built-in Alarm for Test-Set Overfitting
Takashi Ishida, Thanawat Lodkaew, Ikko Yamane
Publishing a large language model (LLM) benchmark (especially its ground-truth answers) on the Internet risks contaminating future LLMs and enabling evaluation gaming: it may be un…
Convergence rate for Nearest Neighbour matching: geometry of the domain and higher-order regularity
Simon Viel, Lionel Truquet, Ikko Yamane
Estimating some mathematical expectations from partially observed data and in particular missing outcomes is a central problem encountered in numerous fields such as transfer learn…
Nearest Neighbor Sampling for Covariate Shift Adaptation
François Portier, Lionel Truquet, Ikko Yamane
Many existing covariate shift adaptation methods estimate sample weights given to loss values to mitigate the gap between the source and the target distribution. However, estimatin…
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 +2
There is a fundamental limitation in the prediction performance that a machine learning model can achieve due to the inevitable uncertainty of the prediction target. In classificat…
Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences
Ikko Yamane, Junya Honda, Florian Yger +1
Ordinary supervised learning is useful when we have paired training data of input and output . However, such paired data can be difficult to collect in practice. In this pap…
A One-step Approach to Covariate Shift Adaptation
Tianyi Zhang, Ikko Yamane, Nan Lu +1
A default assumption in many machine learning scenarios is that the training and test samples are drawn from the same probability distribution. However, such an assumption is often…
Do We Need Zero Training Loss After Achieving Zero Training Error?
Takashi Ishida, Ikko Yamane, Tomoya Sakai +2
Overparameterized deep networks have the capacity to memorize training data with zero \emph{training error}. Even after memorization, the \emph{training loss} continues to approach…
Uplift Modeling from Separate Labels
Ikko Yamane, Florian Yger, Jamal Atif +1
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 (a…
Regularized Multi-Task Learning for Multi-Dimensional Log-Density Gradient Estimation
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. A naive two-step…