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A Universal Unbiased Method for Classification from Aggregate Observations
Zixi Wei · Lei Feng · Bo Han · Tongliang Liu · Gang Niu · Xiaofeng Zhu · Heng Tao Shen

Wed Jul 26 02:00 PM -- 03:30 PM (PDT) @ Exhibit Hall 1 #318

In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances. CFAO is a generalized learning framework that contains various learning problems, such as multiple-instance learning and learning from label proportions. The goal of this paper is to present a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary losses---previous research failed to achieve this goal. Practically, our method works by weighing the importance of each instance and each label in the group, which provides purified supervision for the classifier to learn. Theoretically, our proposed method not only guarantees the risk consistency due to the unbiased risk estimator but also can be compatible with arbitrary losses. Extensive experiments on various problems of CFAO demonstrate the superiority of our proposed method.

Author Information

Zixi Wei (Chongqing University)
Lei Feng (Nanyang Technological University, Singapore)
Tongliang Liu (The University of Sydney)
Gang Niu (RIKEN)
Gang Niu

Gang Niu is currently an indefinite-term senior research scientist at RIKEN Center for Advanced Intelligence Project.

Xiaofeng Zhu (University of Electronic Science and Technology of China)
Heng Tao Shen (University of Electronic Science and Technology of China)

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