7 papers
Partial AUC Maximization from Positive-unlabeled Data
Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama +3
The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarizes true positive rates within a…
Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3
Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors. It is a general framework because it includes…
AUC Maximization from Biased Positive-unlabeled Data with Confidence
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification. Although positive and negative data are requ…
Cross-device Collaborative Test-time Adaptation with Zeroth-order Optimization and Model Merging
Yu Mitsuzumi, Akisato Kimura, Yasuhiro Fujiwara +1
Test-time adaptation (TTA) mitigates domain shifts by using incoming test data to update a model on the fly. The majority of TTA methods require resource-intensive backpropagation…
Meta-learning Representations for Learning from Multiple Annotators
Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama +2
We propose a meta-learning method for learning from multiple noisy annotators. In many applications such as crowdsourcing services, labels for supervised learning are given by mult…
Meta-learning for Positive-unlabeled Classification
Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara
We propose a meta-learning method for positive and unlabeled (PU) classification, which improves the performance of binary classifiers obtained from only PU data in unseen target t…
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi +2
Regularized discrete optimal transport (OT) is a powerful tool to measure the distance between two discrete distributions that have been constructed from data samples on two differ…