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Empirical risk minimization (ERM), with proper loss function and regularization, is the common practice of supervised classification.
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Tighter and convex maximum margin clustering
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Dataset Shift in Machine Learning
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Discriminative clustering by regularized information maximization
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Foundations of Machine Learning
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Training convolutional networks with noisy labels
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Deep Learning
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Deep residual learning for image recognition
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Classification with noisy labels by importance reweighting
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Learning from binary labels with instance-dependent corruption
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Distributional smoothing with virtual adversarial training
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Clustering unclustered data: Unsupervised binary labeling of two datasets having different class balances
M. C. du Plessis, G. Niu, and M. Sugiyama · 2013
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Learning with noisy labels
N. Natarajan, I. S. Dhillon, P. Ravikumar, and A. Tewari · 2013
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Squared-loss mutual information regularization: A novel information-theoretic approach to semi-supervised learning
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Classification with asymmetric label noise: Consistency and maximal denoising
C. Scott, G. Blanchard, and G. Handy · 2013
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∝ \propto SVM for learning with label proportions
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Analysis of learning from positive and unlabeled data
M. C. du Plessis, G. Niu, and M. Sugiyama · 2014
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Theoretical comparisons of positive-unlabeled learning against positive-negative learning
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Learning with bounded instance- and label-dependent label noise
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Training deep neural-networks using a noise adaptation layer
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Learning discrete representations via information maximizing self augmented training
W. Hu, T. Miyato, S. Tokui, E. Matsumoto, and M. Sugiyama · 2017
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Positive-unlabeled learning with non-negative risk estimator
R. Kiryo, G. Niu, M. C. du Plessis, and M. Sugiyama · 2017
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Temporal ensembling for semi-supervised learning
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Making deep neural networks robust to label noise: A loss correction approach
G. Patrini, A. Rozza, A. K. Menon, R. Nock, and L. Qu · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Classification from pairwise similarity and unlabeled data
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Semi-supervised learning via compact latent space clustering
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Learning to reweight examples for robust deep learning
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A theory of learning with corrupted labels
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