4 citations · 4 across the 4 of their papers we have counts for
6 papers
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…
Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +2
Diffusion models are powerful generative models but often generate sensitive data that are unwanted by users, mainly because the unlabeled training data frequently contain such sen…
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +1
Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabeled data, has attrac…
Autoencoding Binary Classifiers for Supervised Anomaly Detection
Yuki Yamanaka, Tomoharu Iwata, Hiroshi Takahashi +2
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervi…
Variational Autoencoder with Implicit Optimal Priors
Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka +2
The variational autoencoder (VAE) is a powerful generative model that can estimate the probability of a data point by using latent variables. In the VAE, the posterior of the laten…