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The scalability of the labeling process and the attainable quality of labels have become limiting factors for many applications of machine learning.
Learning dependency structures for weak supervision models
Varma, P., Sala, F., He, A., Ratner, A., and Ré, C. (2019) · 1903
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Protein function in the post-genomic era
Eisenberg, D., Marcotte, E. M., Xenarios, I., and Yeates, T. O. (2000) · 2000
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Constrained k-means clustering with background knowledge
Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S., et al. (2001) · 2001
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Semi-supervised clustering by seeding
Basu, S., Banerjee, A., and Mooney, R. (2002) · 2002
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From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering
Klein, D., Kamvar, S. D., and Manning, C. D. (2002) · 2002
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Intelligent Clustering with Instance-level Constraints
Wagstaff, K. L. (2002) · 2002
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Learning distance functions using equivalence relations
Bar-Hillel, A., Hertz, T., Shental, N., and Weinshall, D. (2003) · 2003
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A probabilistic framework for semi-supervised clustering
Basu, S., Bilenko, M., and Mooney, R. J. (2004) · 2004
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Integrating constraints and metric learning in semi-supervised clustering
Bilenko, M., Basu, S., and Mooney, R. J. (2004) · 2004
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Active learning with feedback on features and instances
Raghavan, H., Madani, O., and Jones, R. (2006) · 2006
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An adaptive kernel method for semi-supervised clustering
Yan, B. and Domeniconi, C. (2006) · 2006
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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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Relative attributes
Parikh, D. and Grauman, K. (2011) · 2011
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Closing the loop: Fast, interactive semi-supervised annotation with queries on features and instances
Settles, B. (2011) · 2011
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Learning the structure of generative models without labeled data
Bach, S. H., He, B., Ratner, A., and Ré, C. (2017) · 2017
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Learning with feature feedback: from theory to practice
Poulis, S. and Dasgupta, S. (2017) · 2017
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Noise-tolerant interactive learning using pairwise comparisons
Xu, Y., Zhang, H., Miller, K., Singh, A., and Dubrawski, A. (2017) · 2017
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Learning from discriminative feature feedback
Dasgupta, S., Dey, A., Roberts, N., and Sabato, S. (2018) · 2018
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Batch effects in single-cell rna-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T., Morgan, M. D., and Marioni, J. C. (2018) · 2018
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Nonparametric regression with comparisons: Escaping the curse of dimensionality with ordinal information
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Data programming: Creating large training sets, quickly
Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., and Ré, C. (2016) · 2016
Cited alongside, same era.
Socratic learning: Augmenting generative models to incorporate latent subsets in training data
Varma, P., He, B., Iter, D., Xu, P., Yu, R., De Sa, C., and Ré, C. (2016) · 2016
Cited alongside, same era.
Snorkel metal: Weak supervision for multi-task learning
Ratner, A., Hancock, B., Dunnmon, J., Goldman, R., and Ré, C. (2018a)
Cited in the paper.
Training complex models with multi-task weak supervision
Ratner, A., Hancock, B., Dunnmon, J., Sala, F., Pandey, S., and Ré, C. (2018b)
Cited in the paper.
Xu, Y., Muthakana, H., Balakrishnan, S., Singh, A., and Dubrawski, A. (2018) · 2018
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Clustering-driven deep embedding with pairwise constraints
Fogel, S., Averbuch-Elor, H., Cohen-Or, D., and Goldberger, J. (2019) · 2019
Closest in time.
Multi-resolution weak supervision for sequential data
Sala, F., Varma, P., Fries, J., Fu, D. Y., Sagawa, S., Khattar, S., Ramamoorthy, A., Xiao, K., Fatahalian, K., Priest, J., and Re, C. (2019) · 2019
Closest in time.