Fetching the paper…
Reading the bibliography…
Learning with noisy labels is a common challenge in supervised learning.
Learning linear threshold functions in the presence of classification noise
Bylander, T · 1994
Earlier work this paper cites.
Sample-efficient strategies for learning in the presence of noise
Cesa-Bianchi, N., Dichterman, E., Fischer, P., Shamir, E., and Simon, H. U · 1999
Earlier work this paper cites.
Building text classifiers using positive and unlabeled examples
Liu, B., Dai, Y., Li, X., Lee, W. S., and Yu, P. S · 2003
Earlier work this paper cites.
A bayesian truth serum for subjective data
Prelec, D · 2004
Earlier work this paper cites.
Eliciting informative feedback: The peer-prediction method
Miller, N., Resnick, P., and Zeckhauser, R · 2005
Earlier work this paper cites.
Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
Earlier work this paper cites.
Noise tolerant variants of the perceptron algorithm
Khardon, R. and Wachman, G · 2007
Earlier work this paper cites.
Agnostic online learning
Ben-David, S., Pál, D., and Shalev-Shwartz, S · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Learning svms from sloppily labeled data
Stempfel, G. and Ralaivola, L · 2009
Earlier work this paper cites.
Online learning of noisy data
Cesa-Bianchi, N., Shalev-Shwartz, S., and Shamir, O · 2011
Earlier work this paper cites.
Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
Earlier work this paper cites.
A robust bayesian truth serum for small populations
Witkowski, J. and Parkes, D · 2012
Earlier work this paper cites.
Crowdsourced judgement elicitation with endogenous proficiency
Dasgupta, A. and Ghosh, A · 2013
Earlier work this paper cites.
Clustering unclustered data: Unsupervised binary labeling of two datasets having different class balances
Du Plessis, M. C., Niu, G., and Sugiyama, M · 2013
Earlier work this paper cites.
Noise tolerance under risk minimization
Manwani, N. and Sastry, P · 2013
Earlier work this paper cites.
Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A · 2013
Cited alongside, same era.
A robust bayesian truth serum for non-binary signals
Radanovic, G. and Faltings, B · 2013
Cited alongside, same era.
Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., Handy, G., Pozzi, S., and Flaska, M · 2013
Cited alongside, same era.
Dwelling on the Negative: Incentivizing Effort in Peer Prediction
Witkowski, J., Bachrach, Y., Key, P., and Parkes, D. C · 2013
Cited alongside, same era.
Learning from noisy labels with deep neural networks
Sukhbaatar, S. and Fergus, R · 2014
Cited alongside, same era.
Making risk minimization tolerant to label noise
Ghosh, A., Manwani, N., and Sastry, P · 2015
Machine Learning aided Peer Prediction
Liu, Y. and Chen, Y · 2017
Later among the works it cites.
Making deep neural networks robust to label noise: A loss correction approach
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L · 2017
Later among the works it cites.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
Later among the works it cites.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M · 2018
Later among the works it cites.
Deep bilevel learning
Jenni, S. and Favaro, P · 2018
Later among the works it cites.
Water from two rocks: Maximizing the mutual information
Kong, Y. and Schoenebeck, G · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Learning from corrupted binary labels via class-probability estimation
Menon, A., Van Rooyen, B., Ong, C. S., and Williamson, B · 2015
Cited alongside, same era.
A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
Cited alongside, same era.
Training deep neural-networks using a noise adaptation layer
Goldberger, J. and Ben-Reuven, E · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2016
Cited alongside, same era.
Later among the works it cites.
On the minimal supervision for training any binary classifier from only unlabeled data
Lu, N., Niu, G., Menon, A. K., and Sugiyama, M · 2018
Later among the works it cites.
Generalized cross entropy loss for training deep neural networks with noisy labels, 2018
Zhang, Z. and Sabuncu, M. R · 2018
Later among the works it cites.
Robust bi-tempered logistic loss based on bregman divergences
Amid, E., Warmuth, M. K., Anil, R., and Koren, T · 2019
Closest in time.
On symmetric losses for learning from corrupted labels
Charoenphakdee, N., Lee, J., and Sugiyama, M · 2019
Closest in time.
Selfie: Refurbishing unclean samples for robust deep learning
Song, H., Kim, M., and Lee, J.-G · 2019
Closest in time.
L_dmi: An information-theoretic noise-robust loss function
Xu, Y., Cao, P., Kong, Y., and Wang, Y · 2019
Closest in time.
Probabilistic end-to-end noise correction for learning with noisy labels
Yi, K. and Wu, J · 2019
Closest in time.
Learning with bounded instance-and label-dependent label noise
Cheng, J., Liu, T., Ramamohanarao, K., and Tao, D · 2020
Closest in time.
Peer loss functions: Learning from noisy labels without knowing noise rates
Liu, Y. and Guo, H · 2020
Closest in time.
Parts-dependent label noise: Towards instance-dependent label noise, 2020
Xia, X., Liu, T., Han, B., Wang, N., Gong, M., Liu, H., Niu, G., Tao, D., and Sugiyama, M · 2020
Closest in time.