80 citations · 243 across the 11 of their papers we have counts for
Showing 2017Show all
3 papers · 1 filter
stat.ML2017★ 3 cited
Information-Theoretic Representation Learning for Positive-Unlabeled Classification
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically…
stat.ML2017★ 54 cited
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Tomoya Sakai, Gang Niu, Masashi Sugiyama
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classification. So far, various supervised AUC optimization methods…
cs.LG2017
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
Han Bao, Tomoya Sakai, Issei Sato +1
Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as bags) are composed of sub-elements (referred to as instances)…