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stat.ML2018
Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida, Gang Niu, Aditya Krishna Menon +1
In contrast to the standard classification paradigm where the true class is given to each training pattern, complementary-label learning only uses training patterns each equipped w…
stat.ML2018
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
Nan Lu, Gang Niu, Aditya Krishna Menon +1
Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification. In this paper, we study training arbitrary (fr…