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Weak supervision is a popular method for building machine learning models without relying on ground truth annotations.
Maximum likelihood estimation of observer error-rates using the EM algorithm
Dawid, A. P. and Skene, A. M · 1979
Earlier work this paper cites.
Automatic acquisition of hyponyms from large text corpora
Hearst, M. A · 1992
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Graphical Models
Lauritzen, S · 1996
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Assouad, fano, and le cam
Yu, B · 1997
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Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T · 1998
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Constructing biological knowledge bases by extracting information from text sources
Craven, M., Kumlien, J., et al · 1999
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The complexity of learning according to two models of a drifting environment
Long, P. M · 1999
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Prediction, learning, and games
Cesa-Bianchi, N. and Lugosi, G · 2006
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Complexity of inference in graphical models
Chandrasekaran, V., Srebro, N., and Harsha, P · 2008
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Crowdsourcing user studies with mechanical turk
Kittur, A., Chi, E. H., and Suh, B · 2008
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Utility data annotation with amazon mechanical turk
Sorokin, A. and Forsyth, D · 2008
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Graphical models, exponential families, and variational inference
Wainwright, M. J. and Jordan, M. I · 2008
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Modeling annotators: A generative approach to learning from annotator rationales
Zaidan, O. F. and Eisner, J · 2008
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Probabilistic graphical models: principles and techniques
Koller, D. and Friedman, N · 2009
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Learning from measurements in exponential families
Liang, P., Jordan, M. I., and Klein, D · 2009
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Distant supervision for relation extraction without labeled data
Mintz, M., Bills, S., Snow, R., and Jurafsky, D · 2009
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Are your participants gaming the system? screening mechanical turk workers
Downs, J. S., Holbrook, M. B., Sheng, S., and Cranor, L. F · 2010
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Generalized expectation criteria for semi-supervised learning with weakly labeled data
Mann, G. S. and McCallum, A · 2010
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Collecting image annotations using amazon’s mechanical turk
Rashtchian, C., Young, P., Hodosh, M., and Hockenmaier, J · 2010
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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
Cited alongside, same era.
Iterative learning for reliable crowdsourcing systems
Karger, D. R., Oh, S., and Shah, D · 2011
Cited alongside, same era.
Designing incentives for inexpert human raters
Shaw, A. D., Horton, J. J., and Chen, D. L · 2011
Cited alongside, same era.
Deepdive: Web-scale knowledge-base construction using statistical learning and inference
Niu, F., Zhang, C., Ré, C., and Shavlik, J. W · 2012
Cited alongside, same era.
Online learning and online convex optimization
Shalev-Shwartz, S. et al · 2012
Cited alongside, same era.
Reducing wrong labels in distant supervision for relation extraction
Takamatsu, S., Sato, I., and Nakagawa, H · 2012
Cited alongside, same era.
Constrained deep weak supervision for histopathology image segmentation
Jia, Z., Huang, X., Eric, I., Chang, C., and Xu, Y · 2017
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DeepDive: Declarative knowledge base construction
Zhang, C., Ré, C., Cafarella, M., De Sa, C., Ratner, A., Shin, J., Wang, F., and Wu, S · 2017
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https://data.world/crowdflower/weather-sentiment, 2018
Weather sentiment: Dataset in crowdflower · 2018
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https://archive.org/details/tv, 2018
Internet archive: Tv news archive · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2018
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Who said what: Modeling individual labelers improves classification
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Evaluating the crowd with confidence
Joglekar, M., Garcia-Molina, H., and Parameswaran, A · 2013
Cited alongside, same era.
Tensor decompositions for learning latent variable models
Anandkumar, A., Ge, R., Hsu, D., Kakade, S. M., and Telgarsky, M · 2014
Cited alongside, same era.
Estimating latent-variable graphical models using moments and likelihoods
Chaganty, A. T. and Liang, P · 2014
Cited alongside, same era.
Improved pattern learning for bootstrapped entity extraction
Gupta, S. and Manning, C · 2014
Cited alongside, same era.
Tubespam: Comment spam filtering on youtube
Alberto, T. C., Lochter, J. V., and Almeida, T. A · 2015
Cited alongside, same era.
On the sample covariance matrix estimator of reduced effective rank population matrices, with applications to fpca
Bunea, F. and Xiao, L · 2015
Cited alongside, same era.
Guan, M. Y., Gulshan, V., Dai, A. M., and Hinton, G. E · 2018
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Learning from noisy singly-labeled data
Khetan, A., Lipton, Z. C., and Anandkumar, A · 2018
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Exploring the limits of weakly supervised pretraining
Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., and van der Maaten, L · 2018
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Snorkel: Rapid training data creation with weak supervision
Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., and Ré, C · 2018
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On the fenchel duality between strong convexity and lipschitz continuous gradient
Zhou, X · 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., Alborzi, H., et al · 2019
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Rekall: Specifying video events using compositions of spatiotemporal labels
Fu, D. Y., Crichton, W., Hong, J., Yao, X., Zhang, H., Truong, A., Narayan, A., Agrawala, M., Ré, C., and Fatahalian, K · 2019
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Online model distillation for efficient video inference
Mullapudi, R. T., Chen, S., Zhang, K., Ramanan, D., and Fatahalian, K · 2019
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Training complex models with multi-task weak supervision
Ratner, A. J., Hancock, B., Dunnmon, J., Sala, F., Pandey, S., and Ré, C · 2019
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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 Ré, C · 2019
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Generating multi-agent trajectories using programmatic weak supervision
Zhan, E., Zheng, S., Yue, Y., Sha, L., and Lucey, P · 2019
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Overton: A data system for monitoring and improving machine-learned products
Ré, C., Niu, F., Gudipati, P., and Srisuwananukorn, C · 2020
Closest in time.
Weakly supervised sequence tagging from noisy rules
Safranchik, E., Luo, S., and Bach, S. H · 2020
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Migrating a privacy-safe information extraction system to a software 2.0 design
Sheng, Y., Vo, N. H., Wendt, J. B., Tata, S., and Najork, M · 2020
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