Machine truth serum: a surprisingly popular approach to improving ensemble methods

[1]  Diyi Yang,et al.  MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification , 2020, ACL.

[2]  Chen Gong,et al.  Deep Discriminative CNN with Temporal Ensembling for Ambiguously-Labeled Image Classification , 2020, AAAI.

[3]  David Berthelot,et al.  FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence , 2020, NeurIPS.

[4]  Ruslan Salakhutdinov,et al.  Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function , 2019, AAAI.

[5]  Diyi Yang,et al.  Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms , 2019, NAACL.

[6]  David Berthelot,et al.  MixMatch: A Holistic Approach to Semi-Supervised Learning , 2019, NeurIPS.

[7]  Quoc V. Le,et al.  Unsupervised Data Augmentation for Consistency Training , 2019, NeurIPS.

[8]  Yannis Avrithis,et al.  Label Propagation for Deep Semi-Supervised Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Joost van de Weijer,et al.  Exploiting Unlabeled Data in CNNs by Self-Supervised Learning to Rank , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Jian Yang,et al.  Ensemble Teaching for Hybrid Label Propagation , 2019, IEEE Transactions on Cybernetics.

[11]  Quoc V. Le,et al.  Semi-Supervised Sequence Modeling with Cross-View Training , 2018, EMNLP.

[12]  Colin Raffel,et al.  Realistic Evaluation of Deep Semi-Supervised Learning Algorithms , 2018, NeurIPS.

[13]  Sebastian Ruder,et al.  Universal Language Model Fine-tuning for Text Classification , 2018, ACL.

[14]  Hongyi Zhang,et al.  mixup: Beyond Empirical Risk Minimization , 2017, ICLR.

[15]  Shin Ishii,et al.  Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Harri Valpola,et al.  Weight-averaged consistency targets improve semi-supervised deep learning results , 2017, ArXiv.

[17]  H. Sebastian Seung,et al.  A solution to the single-question crowd wisdom problem , 2017, Nature.

[18]  Quoc V. Le,et al.  Semi-supervised Sequence Learning , 2015, NIPS.

[19]  Xiang Zhang,et al.  Character-level Convolutional Networks for Text Classification , 2015, NIPS.

[20]  Tapani Raiko,et al.  Semi-supervised Learning with Ladder Networks , 2015, NIPS.

[21]  Carolyn Penstein Rosé,et al.  Weakly Supervised Role Identification in Teamwork Interactions , 2015, ACL.

[22]  François Laviolette,et al.  Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm , 2015, J. Mach. Learn. Res..

[23]  Qiang Liu,et al.  Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy , 2014, ICML.

[24]  Xi Chen,et al.  Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing , 2014, J. Mach. Learn. Res..

[25]  M. G. Morgan.  Use (and abuse) of expert elicitation in support of decision making for public policy , 2014, Proceedings of the National Academy of Sciences.

[26]  John C. Platt,et al.  Learning from the Wisdom of Crowds by Minimax Entropy , 2012, NIPS.

[27]  Jian Peng,et al.  Variational Inference for Crowdsourcing , 2012, NIPS.

[28]  S. Frederick,et al.  Intuitive Biases in Choice versus Estimation: Implications for the Wisdom of Crowds , 2011 .

[29]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[30]  Gerardo Hermosillo,et al.  Learning From Crowds , 2010, J. Mach. Learn. Res..

[31]  Ming-Wei Chang,et al.  Importance of Semantic Representation: Dataless Classification , 2008, AAAI.

[32]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[33]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[34]  D. Prelec.  A Bayesian Truth Serum for Subjective Data , 2004, Science.

[35]  Bernardo A. Huberman,et al.  Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms , 2004, Manag. Sci..

[36]  Erik T. Mueller,et al.  Open Mind Common Sense: Knowledge Acquisition from the General Public , 2002, OTM.

[37]  C. Y. Peng,et al.  An Introduction to Logistic Regression Analysis and Reporting , 2002 .

[38]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[39]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[40]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[41]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[42]  Michael Vitale,et al.  The wisdom of crowds , 2016, The Lancet.

[43]  Dong-Hyun Lee,et al.  Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks , 2013 .

[44]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .