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We study the effect of imperfect training data labels on the performance of classification methods.
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Blanchard, G., Flaska, M., Handy, G., Pozzi, S. & Scott, C. (2016) Classification with asymmetric label noise: consistency and maximal denoising · 2016
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Patrini, G., Nielsen, F., Nock, R. & Carioni, M. (2016) Loss factorization, weakly supervised learning and label noise robustness · 2016
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Cheng, J., Liu, T., Ramamohanarao, K. & Tao, D. (2017) Learning with bounded instance- and label-dependent label noise · 2017
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NIPS 2017
Inouye, D. I., Ravikumar, P., Das, P. & Dutta, A. (2017) Hyperparameter selection under localized label noise via corrupt validation · 2017
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Uncertainty in Artificial Intelligence 2017
Northcutt, C. G., Wu, T. & Chuang, I. L. (2017) Learning with confident examples: Rank pruning for robust classification with noisy labels · 2017
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Cannings, T. I., Berrett, T. B. & Samworth, R. J. (2018) Local nearest neighbour classification with applications to semi-supervised learning · 2018
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Celisse, A. & Mary-Huard, T. (2018) Theoretical analysis of cross-validation for estimating the risk of the k k -nearest neighbor classifier · 2018
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