136 citations · 364 across the 8 of their papers we have counts for
8 papers
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo, Gang Niu, Marthinus C. du Plessis +1
From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model i…
Class-prior Estimation for Learning from Positive and Unlabeled Data
Marthinus C. du Plessis, Gang Niu, Masashi Sugiyama
We consider the problem of estimating the class prior in an unlabeled dataset. Under the assumption that an additional labeled dataset is available, the class prior can be estimate…
Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu +1
Most of the semi-supervised classification methods developed so far use unlabeled data for regularization purposes under particular distributional assumptions such as the cluster a…
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
Gang Niu, Marthinus Christoffel du Plessis, Tomoya Sakai +2
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning…
Transductive Learning with Multi-class Volume Approximation
Gang Niu, Bo Dai, Marthinus Christoffel du Plessis +1
Given a hypothesis space, the large volume principle by Vladimir Vapnik prioritizes equivalence classes according to their volume in the hypothesis space. The volume approximation…
Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances
Marthinus Christoffel du Plessis, Masashi Sugiyama
We consider the unsupervised learning problem of assigning labels to unlabeled data. A naive approach is to use clustering methods, but this works well only when data is properly c…
Density-Difference Estimation
Masashi Sugiyama, Takafumi Kanamori, Taiji Suzuki +3
We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and the…
Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching
Marthinus Du Plessis, Masashi Sugiyama
In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias.…