Fetching the paper…
Reading the bibliography…
Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limited.
Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
David Berthelot, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, and Colin Raffel · 1911
Earlier work this paper cites.
Self-supervised learning for semi-supervised temporal action proposal
Xiang Wang, Shiwei Zhang, Zhiwu Qing, Yuanjie Shao, Changxin Gao, and Nong Sang · 1914
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet, Yoshua Bengio, et al · 2005
Earlier work this paper cites.
Semi-supervised learning
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2006
Earlier work this paper cites.
Importance of semantic representation: Dataless classification
Ming-Wei Chang, Lev-Arie Ratinov, Dan Roth, and Vivek Srikumar · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
Earlier work this paper cites.
Semi-supervised sequence learning
Andrew M Dai and Quoc V Le · 2015
Earlier work this paper cites.
Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2015
Earlier work this paper cites.
Semi-supervised learning with ladder networks
Antti Rasmus, Harri Valpola, Mikko Honkala, Mathias Berglund, and Tapani Raiko · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
Earlier work this paper cites.
Learning statistical models of phenotypes using noisy labeled training data
Vibhu Agarwal, Tanya Podchiyska, Juan M Banda, Veena Goel, Tiffany I Leung, Evan P Minty, Timothy E Sweeney, Elsie Gyang, and Nigam H Shah · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2016
Earlier work this paper cites.
Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow · 2016
Earlier work this paper cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
Earlier work this paper cites.
Antti Tarvainen and Harri Valpola · 2017
Earlier work this paper cites.
Improved variational autoencoders for text modeling using dilated convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick · 2017
Earlier work this paper cites.
Fairness constraints: Mechanisms for fair classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
Earlier work this paper cites.
Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, et al · 2018
Earlier work this paper cites.
Semi-supervised sequence modeling with cross-view training
Kevin Clark, Minh-Thang Luong, Christopher D Manning, and Quoc V Le · 2018
Earlier work this paper cites.
The measure and mismeasure of fairness: A critical review of fair machine learning
Sam Corbett-Davies and Sharad Goel · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Cited alongside, same era.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
Cited alongside, same era.
Weakly-supervised neural text classification
Yu Meng, Jiaming Shen, Chao Zhang, and Jiawei Han · 2018
Cited alongside, same era.
Early-learning regularization prevents memorization of noisy labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda · 2020
Later among the works it cites.
Peer loss functions: Learning from noisy labels without knowing noise rates
Yang Liu and Hongyi Guo · 2020
Later among the works it cites.
Minimax pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertran, and Guillermo Sapiro · 2020
Later among the works it cites.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
Later among the works it cites.
Seminll: A framework of noisy-label learning by semi-supervised learning
Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, and Guodong Long · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
Cited alongside, same era.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer · 2018
Cited alongside, same era.
Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
Cited alongside, same era.
mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
A brief introduction to weakly supervised learning
Zhi-Hua Zhou · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Variational sequential labelers for semi-supervised learning
Mingda Chen, Qingming Tang, Karen Livescu, and Kevin Gimpel · 2019
Cited alongside, same era.
Understanding and improving early stopping for learning with noisy labels
Yingbin Bai, Erkun Yang, Bo Han, Yanhua Yang, Jiatong Li, Yinian Mao, Gang Niu, and Tongliang Liu · 2021
Closest in time.
Learning with instance-dependent label noise: A sample sieve approach
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2021
Closest in time.
Multiaccurate proxies for downstream fairness
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, and Saeed Sharifi-Malvajerdi · 2021
Closest in time.
Addressing bias and fairness in search systems
Ruoyuan Gao and Chirag Shah · 2021
Closest in time.
Disparate impact in item recommendation: A case of geographic imbalance
Elizabeth Gómez, Ludovico Boratto, and Maria Salamó · 2021
Closest in time.
Universal semi-supervised learning
Zhuo Huang, Chao Xue, Bo Han, Jian Yang, and Chen Gong · 2021
Closest in time.
Jigsaw Toxicity dataset: Toxic comment classification challenge
Kaggle · 2021
Closest in time.
Translation tutorial: Fairness and friends
Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich · 2021
Closest in time.
Understanding instance-level label noise: Disparate impacts and treatments
Yang Liu · 2021
Closest in time.
Can less be more? when increasing-to-balancing label noise rates considered beneficial
Yang Liu and Jialu Wang · 2021
Closest in time.
On the consistency training for open-set semi-supervised learning
Huixiang Luo, Hao Cheng, Yuting Gao, Ke Li, Mengdan Zhang, Fanxu Meng, Xiaowei Guo, Feiyue Huang, and Xing Sun · 2021
Closest in time.
Unintended selection: Persistent qualification rate disparities and interventions
Reilly Raab and Yang Liu · 2021
Closest in time.
Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2021
Closest in time.
When optimizing $f$-divergence is robust with label noise
Jiaheng Wei and Yang Liu · 2021
Closest in time.
Dash: Semi-supervised learning with dynamic thresholding
Yi Xu, Lei Shang, Jinxing Ye, Qi Qian, Yu-Feng Li, Baigui Sun, Hao Li, and Rong Jin · 2021
Closest in time.
Identifiability of label noise transition matrix
Yang Liu · 2022
Closest in time.
Assessing multilingual fairness in pre-trained multimodal representations
Jialu Wang, Yang Liu, and Xin Eric Wang · 2022
Closest in time.
Learning with noisy labels revisited: A study using real-world human annotations
Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu · 2022
Closest in time.