3 papers
cs.LG2016
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…
cs.LG2015
Online Markov decision processes with policy iteration
Yao Ma, Hao Zhang, Masashi Sugiyama
The online Markov decision process (MDP) is a generalization of the classical Markov decision process that incorporates changing reward functions. In this paper, we propose practic…
cs.LG2015
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing
Hao Zhang, Yao Ma, Masashi Sugiyama
We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget. A challenge in this scenario is ho…