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Poster
Provably Consistent Partial-Label Learning
Lei Feng · Jiaqi Lv · Bo Han · Miao Xu · Gang Niu · Xin Geng · Bo An · Masashi Sugiyama

Wed Dec 09 09:00 AM -- 11:00 AM (PST) @ Poster Session 3 #1012

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods - none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and develop two PLL methods that are guaranteed to be provably consistent, i.e., one is risk-consistent and the other is classifier-consistent. Our methods are advantageous, since they are compatible with any deep network or stochastic optimizer. Furthermore, thanks to the generation model, we would be able to answer the two questions above by testing if the generation model matches given candidate label sets. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed generation model and two PLL methods.

Author Information

Lei Feng (Nanyang Technological University)
Jiaqi Lv (Southeast University)
Bo Han (HKBU / RIKEN)
Miao Xu (RIKEN AIP)
Gang Niu (RIKEN)
Xin Geng (Southeast University)
Bo An (Nanyang Technological University)
Masashi Sugiyama (RIKEN / University of Tokyo)

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