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Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo · Gang Niu · Marthinus C du Plessis · Masashi Sugiyama

Tue Dec 05 10:55 AM -- 11:10 AM (PST) @ Hall A

From only \emph{positive}~(P) and \emph{unlabeled}~(U) data, a binary classifier can be trained with PU learning, in which the state of the art is \emph{unbiased PU learning}. However, if its model is very flexible, its empirical risk on training data will go negative and we will suffer from serious overfitting. In this paper, we propose a \emph{non-negative risk estimator} for PU learning. When being minimized, it is more robust against overfitting and thus we are able to train very flexible models given limited P data. Moreover, we analyze the \emph{bias}, \emph{consistency} and \emph{mean-squared-error reduction} of the proposed risk estimator and the \emph{estimation error} of the corresponding risk minimizer. Experiments show that the proposed risk estimator successfully fixes the overfitting problem of its unbiased counterparts.

Author Information

Ryuichi Kiryo (UTokyo/RIKEN)
Gang Niu (RIKEN)

Gang Niu is currently a research scientist (indefinite-term) at RIKEN Center for Advanced Intelligence Project. He received the PhD degree in computer science from Tokyo Institute of Technology in 2013. Before joining RIKEN as a research scientist, he was a senior software engineer at Baidu and then an assistant professor at the University of Tokyo. He has published more than 70 journal articles and conference papers, including 14 NeurIPS (1 oral and 3 spotlights), 28 ICML, and 2 ICLR (1 oral) papers. He has served as an area chair 14 times, including ICML 2019--2021, NeurIPS 2019--2021, and ICLR 2021--2022.

Marthinus C du Plessis (The University of Tokyo)
Masashi Sugiyama (RIKEN / University of Tokyo)

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