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Owing to the prohibitive costs of generating large amounts of labeled data, programmatic weak supervision is a growing paradigm within machine learning.
Stochastic generalized adversarial label learning
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Curriculum Labeling: Self-paced Pseudo-Labeling for Semi-Supervised Learning
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Constrained Labeling for Weakly Supervised Learning
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A survey of label-noise representation learning: Past, present and future
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Aggregating Crowdsourced Binary Ratings
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Minimax Optimal Convergence Rates for Estimating Ground Truth from Crowdsourced Labels
Gao, C.; and Zhou, D. 2013 · 2013
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Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
Lee, D.-H. 2013 · 2013
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Learning with Noisy Labels
Natarajan, N.; Dhillon, I. S.; Ravikumar, P. K.; and Tewari, A. 2013 · 2013
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Data programming: Creating large training sets, quickly
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Learning from crowdsourced labeled data: a survey
Zhang, J.; Wu, X.; and Sheng, V. 2016 · 2016
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Learning the Structure of Generative Models without Labeled Data
Bach, S. H.; He, B. D.; Ratner, A. J.; and Ré, C. 2017 · 2017
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2017 · 2017
A principled approach for learning task similarity in multitask learning
Shui, C.; Abbasi, M.; Robitaille, L.-É.; Wang, B.; and Gagné, C. 2019 · 2019
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Learning from Rules Generalizing Labeled Exemplars
Awasthi, A.; Ghosh, S.; Goyal, R.; and Sarawagi, S. 2020 · 2020
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Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods
Fu, D. Y.; Chen, M. F.; Sala, F.; Hooper, S. M.; Fatahalian, K.; and Ré, C. 2020 · 2020
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Weakly Supervised Sequence Tagging from Noisy Rules
Safranchik, E.; Luo, S.; and Bach, S. H. 2020 · 2020
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Active WeaSuL: Improving Weak Supervision with Active Learning
Biegel, S.; El-Khatib, R.; Oliveira, L. O. V. B.; Baak, M.; and Aben, N. 2021 · 2021
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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. 2017 · 2017
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Sluice networks: Learning what to share between loosely related tasks
Ruder, S.; Bingel, J.; Augenstein, I.; and Søgaard, A. 2017 · 2017
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Trace Norm Regularised Deep Multi-Task Learning
Yang, Y.; and Hospedales, T. M. 2017 · 2017
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Improving Label Noise Filtering by Exploiting Unlabeled Data
Guan, D.; Wei, H.; Yuan, W.; Han, G.; Tian, Y.; Al-Dhelaan, M.; and Al-Dhelaan, A. 2018 · 2018
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Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly
Xian, Y.; Lampert, C. H.; Schiele, B.; and Akata, Z. 2018 · 2018
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Optuna: A Next-generation Hyperparameter Optimization Framework
Akiba, T.; Sano, S.; Yanase, T.; Ohta, T.; and Koyama, M. 2019 · 2019
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Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
Chen, M. F.; Cohen-Wang, B.; Mussmann, S.; Sala, F.; and R’e, C. 2021 · 2021
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Self-training with Weak Supervision
Karamanolakis, G.; Mukherjee, S. S.; Zheng, G.; and Awadallah, A. H. 2021 · 2021
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Meta Pseudo Labels
Pham, H.; Xie, Q.; Dai, Z.; and Le, Q. V. 2021 · 2021
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End-to-End Weak Supervision
Ruhling Cachay, S.; Boecking, B.; and Dubrawski, A. 2021 · 2021
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DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples
Xu, Y.; Ding, J.; Zhang, L.; and Zhou, S. 2021 · 2021
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Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach
Yu, Y.; Zuo, S.; Jiang, H.; Ren, W.; Zhao, T.; and Zhang, C. 2021 · 2021
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WRENCH: A Comprehensive Benchmark for Weak Supervision
Zhang, J.; Yu, Y.; Li, Y.; Wang, Y.; Yang, Y.; Yang, M.; and Ratner, A. 2021 · 2021
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Data Consistency for Weakly Supervised Learning
Arachie, C.; and Huang, B. 2022 · 2022
Closest in time.
Universalizing Weak Supervision
Shin, C.; Li, W.; Vishwakarma, H.; Roberts, N. C.; and Sala, F. 2022 · 2022
Closest in time.
Learning from Multiple Noisy Partial Labelers
Yu, P.; Ding, T.; and Bach, S. H. 2022 · 2022
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