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Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component.
Theory of reproducing kernels
Aronszajn, N · 1950
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Estimating a kernel Fisher discriminant in the presence of label noise
Lawrence, N. and Scholkopf, B · 2001
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Partially supervised classification of text documents
Liu, B., Lee, W. S., Yu, P. S., and Li, X · 2002
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Reproducing kernel Hilbert spaces in Probability and Statistics
Berlinet, A. and Thomas, C · 2004
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Learning from positive and unlabeled examples
Denis, F., Gilleron, R., and Letouzey, F · 2005
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Universal kernels
Michelli, C., Xu, Y., and Zhang, H · 2006
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A Hilbert space embedding for distributions
Smola, A., Gretton, A., Song, L., and Scholkopf, B · 2007
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Learning classifiers from only positive and unlabeled data
Elkan, C. and Noto, K · 2008
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Robust supervised classification with mixture models: Learning from data with uncertain labels
Bouveyron, C. and Girard, S · 2009
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Learning SVMs from sloppily labeled data
Stempfel, G. and Ralaivola, L · 2009
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Presence-only data and the EM algorithm
Ward, G., Hastie, T., Barry, S., Elith, J., and Leathwick, J. R · 2009
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Semi-supervised novelty detection
Blanchard, G., Lee, G., and Scott, C · 2010
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Random classification noise defeats all convex potential boosters
Long, P. and Servido, R · 2010
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Learning from crowds
Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., and Moy, L · 2010
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Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P., and Tewari, A · 2013
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Class prior estimation from positive and unlabeled data
du Plessis, M. C. and Sugiyama, M · 2014
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Class proportion estimation with application to multiclass anomaly rejection
Sanderson, T. and Scott, C · 2014
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Learning from corrupted binary labels via class-probability estimation
Menon, A. K., van Rooyen, B., Ong, C. S., and Williamson, R. C · 2015
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
Scott, C · 2015
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Learning topic models – going beyond SVD
Arora, S., Ge, R., and Moitra, A · 2012
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Scholkopf, B., and Smola, A · 2012
Cited alongside, same era.
Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., and Handy, G
Cited in the paper.
Classification with asymmetric label noise: Consistency and maximal denoising
Scott, C., Blanchard, G., Handy, G., Pozzi, S., and Flaska, M
Cited in the paper.
Jain, S., White, M., Trosset, M. W., and Radivojac, P · 2016
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Classification with noisy labels by importance reweighting
Liu, T. and Tao, D · 2016
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