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Learning by Minimizing the Sum of Ranked Range
Shu Hu · Yiming Ying · xin wang · Siwei Lyu

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #366

In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual losses of a learning model over each training sample, and in the individual loss for multi-label learning, which combines prediction scores over all class labels. In this work, we introduce the sum of ranked range (SoRR) as a general approach to form learning objectives. A ranked range is a consecutive sequence of sorted values of a set of real numbers. The minimization of SoRR is solved with the difference of convex algorithm (DCA). We explore two applications in machine learning of the minimization of the SoRR framework, namely the AoRR aggregate loss for binary classification and the TKML individual loss for multi-label/multi-class classification. Our empirical results highlight the effectiveness of the proposed optimization framework and demonstrate the applicability of proposed losses using synthetic and real datasets.

Author Information

Shu Hu (University at Buffalo, State University of New York)
Yiming Ying (State University of New York at Albany)
xin wang (CuraCloud)
Siwei Lyu (University at Albany)

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