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Curation of large fully supervised datasets has become one of the major roadblocks for machine learning.
Stochastic Generalized Adversarial Label Learning
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Iterative learning for reliable crowdsourcing systems
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Maximum likelihood estimation of observer error-rates using the EM algorithm
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Learning question classifiers
Li, X.; and Roth, D. 2002 · 2002
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Incorporating prior knowledge into boosting
Schapire, R. E.; Rochery, M.; Rahim, M.; and Gupta, N. 2002 · 2002
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Learning from Imperfect Annotations
Platanios, E. A.; Al-Shedivat, M.; Xing, E.; and Mitchell, T. 2020 · 2004
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Co-validation: Using model disagreement on unlabeled data to validate classification algorithms
Madani, O.; Pennock, D. M.; and Flake, G. W. 2005 · 2005
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Learning to extract relations from the web using minimal supervision
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Multilevel Bayesian models of categorical data annotation
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Learning from labeled features using generalized expectation criteria
Druck, G.; Mann, G.; and McCallum, A. 2008 · 2008
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Generalized expectation criteria for semi-supervised learning of conditional random fields
Mann, G. S.; and McCallum, A. 2008 · 2008
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Distant supervision for relation extraction without labeled data
Mintz, M.; Bills, S.; Snow, R.; and Jurafsky, D. 2009 · 2009
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Generalized expectation criteria for semi-supervised learning with weakly labeled data
Mann, G. S.; and McCallum, A. 2010 · 2010
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Modeling relations and their mentions without labeled text
Riedel, S.; Yao, L.; and McCallum, A. 2010 · 2010
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Collective cross-document relation extraction without labelled data
Yao, L.; Riedel, S.; and McCallum, A. 2010 · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J.; Hazan, E.; and Singer, Y. 2011 · 2011
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Harnessing the crowdsourcing power of social media for disaster relief
Gao, H.; Barbier, G.; and Goolsby, R. 2011 · 2011
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Knowledge-based weak supervision for information extraction of overlapping relations
Hoffmann, R.; Zhang, C.; Ling, X.; Zettlemoyer, L.; and Weld, D. S. 2011 · 2011
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Learning word vectors for sentiment analysis
Maas, A. L.; Daly, R. E.; Pham, P. T.; Huang, D.; Ng, A. Y.; and Potts, C. 2011 · 2011
Regularized minimax conditional entropy for crowdsourcing
Zhou, D.; Liu, Q.; Platt, J. C.; Meek, C.; and Shah, N. B. 2015 · 2015
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Clinical Tagging with Joint Probabilistic Models
Halpern, Y.; Horng, S.; and Sontag, D. 2016 · 2016
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Unsupervised ensemble learning with dependent classifiers
Jaffe, A.; Fetaya, E.; Nadler, B.; Jiang, T.; and Kluger, Y. 2016 · 2016
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The Street View House Numbers (SVHN) Dataset
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. 2018 · 2016
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Estimating accuracy from unlabeled data: A Bayesian approach
Platanios, E. A.; Dubey, A.; and Mitchell, T. 2016 · 2016
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Data programming: Creating large training sets, quickly
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Variational inference for crowdsourcing
Liu, Q.; Peng, J.; and Ihler, A. T. 2012 · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R.; Perelygin, A.; Wu, J.; Chuang, J.; Manning, C. D.; Ng, A. Y.; and Potts, C. 2013 · 2013
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Beat the mturkers: Automatic image labeling from weak 3d supervision
Chen, L.-C.; Fidler, S.; Yuille, A. L.; and Urtasun, R. 2014 · 2014
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GloVe: Global vectors for word representation
Pennington, J.; Socher, R.; and Manning, C. 2014 · 2014
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Estimating accuracy from unlabeled data
Platanios, E. A.; Blum, A.; and Mitchell, T. 2014 · 2014
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Tell me what you see and I will show you where it is
Xu, J.; Schwing, A. G.; and Urtasun, R. 2014 · 2014
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Ratner, A. J.; De Sa, C. M.; Wu, S.; Selsam, D.; and Ré, C. 2016 · 2016
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Unsupervised risk estimation using only conditional independence structure
Steinhardt, J.; and Liang, P. S. 2016 · 2016
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Crowdsourcing via Tensor Augmentation and Completion
Zhou, Y.; and He, J. 2016 · 2016
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Learning from noisy singly-labeled data
Khetan, A.; Lipton, Z. C.; and Anandkumar, A. 2017 · 2017
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Snorkel: Fast training set generation for information extraction
Ratner, A. J.; Bach, S. H.; Ehrenberg, H. R.; and Ré, C. 2017 · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
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Snorkel metal: Weak supervision for multi-task learning
Ratner, A.; Hancock, B.; Dunnmon, J.; Goldman, R.; and Ré, C. 2018 · 2018
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Snorkel DryBell: A case study in deploying weak supervision at industrial scale
Bach, S. H.; Rodriguez, D.; Liu, Y.; Luo, C.; Shao, H.; Xia, C.; Sen, S.; Ratner, A.; Hancock, B.; and Alborzi, H. 2019 · 2019
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