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Many machine learning problems can be characterized by mutual contamination models.
The use of multiple measurements in taxonomic problems
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Black/white differences in health status and mortality among the Elderly
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A Probabilistic Theory of Pattern Recognition
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Gradient-based learning applied to document recognition
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Association mapping in structured populations
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Learning with multiple labels
R. Jin and Z. Ghahramani · 2002
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Latent dirichlet allocation
D. Blei, A. Ng, and M. Jordan · 2003
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When does non-negative matrix factorization give a correct decomposition into parts?
D. Donoho and V. Stodden · 2003
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A bayesian hierarchical model for learning natural scene categories
F. Li and P. Perona · 2005
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Classification with partial labels
N. Nyugen and R. Caruana · 2008
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Semi-supervised novelty detection
G. Blanchard, G. Lee, and C. Scott · 2010
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Spectral regularization for support estimation
E. De Vito, L. Rosasco, and A. Toigo · 2010
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Learning from partial labels
T. Cour, B. Sapp, and B. Taskar · 2011
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Learning topic models–going beyond svd
S. Arora, R. Ge, and A. Moitra · 2012
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Proper losses for learning from partial labels
J. Cid-Sueiro · 2012
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A conditional multinomial mixture model for superset label learning
L.-P. Liu and T. G. Dietterich · 2012
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Factoring non-negative matrices with linear programs
B. Recht, C. Re, J. Tropp, and V. Bittorf · 2012
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A practical algorithm for topic modeling with provable guarantees
S. Arora, R. Ge, Y. Halpern, D. Mimno, A. Moitra, D. Sontag, Y. Wu, and M. Zhu · 2013
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Topic discovery through data dependent and random projections
W. Ding, M. Rohban, P. Ishwar, and V. Saligrama · 2013
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Better word representations with recursive neural networks for morphology
M. Luong, R. Socher, and C. Manning · 2013
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Gaussian lda for topic models with word embeddings
R. Das, M. Zaheer, and C. Dyer · 2015
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A rate of convergence for mixture proportion estimation, with application to learning from noisy labels
C. Scott · 2015
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Learning in the presence of corruption
B. van Rooyen and R. Williamson · 2015
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Learning with symmetric label noise: The importance of being unhinged
B. van Rooyen, A. Menon, and R. Williamson · 2015
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Classification with asymmetric label noise: Consistency and maximal denoising
G. Blanchard, M. Flaska, G. Handy, S. Pozzi, and C. Scott · 2016
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Anchor-free correlated topic modeling
K. Huang, X. Fu, and N. D. Sidiropoulos · 2016
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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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Decontamination of mutually contaminated models
G. Blanchard and C. Scott · 2014
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Efficient distributed topic modeling with provable guarantees
W. Ding, M. Rohban, P. Ishwar, and V. Saligrama · 2014
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Learnability of the superset label learning problem
L.-P. Liu and T. G. Dietterich · 2014
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Geometrical and computational aspects of spectral support estimation for novelty detection
A. Rudi, F. Odone, and E. De Vito · 2014
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Nonparametric semi-supervised learning of class proportions
S. Jain, M. White, M. W. Trosset, and P. Radivojac · 2016
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Learning from binary labels with instance-dependent corruption
A. Menon, B. van Rooyen, and N. Natarajan · 2016
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Mixture proportion estimation via kernel embeddings of distributions
H. Ramaswamy, C Scott, and A Tewari · 2016
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UCI machine learning repository, 2017
D. Dheeru and E. K. Taniskidou · 2017
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Robust loss functions under label noise for deep neural networks
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Making deep neural networks robust to label noise: a loss correction approach
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A correlated topic model using word embeddings
G. Xun, Y. Li, W. X. Zhao, J. Gao, and A. Zhang · 2017
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Inter and intra topic structure learning with word embeddings
H. Zhao, L. Du, W. Buntine, and M. Zhou · 2018
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