Democratic co-learning

By Yan Zhou, Sally A. Goldman. 2004

230 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Reinforcement Learning

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

AmbigQA: Answering Ambiguous Open-domain Questions (2020, methodology): “We modify a democratic co-training algorithm (Zhou and Goldman, 2004) as described in Algorithm 1.”
Revisiting Self-Training for Neural Sequence Generation (2019, background): “In the same spirit, democratic co-training (Zhou & Goldman, 2004) or tri-training (Zhou & Li, 2005) trains multiple models with different conﬁgurations on the same data feature set, and a subset of the models act as teachers for others.”
AutoML: A Survey of the State-of-the-Art (2019, methodology): “[67] summarize the self-labeling methods into the following categories: selftraining [68, 69], co-training [70, 71], and co-learning [72].”
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, methodology): “The next class uses multiple learning algorithms and includes Democratic Co-learning [72].”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “To improve the quality of pseudo labels, a common strategy is to train multiple classifiers and find a consensus label, such as using different machine learning algorithms to train models on the same data [274].”
An Overview of Deep Semi-Supervised Learning (2020, unknown): “Democratic Co-training [187].”
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study (2015, methodology): “Standard self-training Self-Training [13] Standard co-training Co-Training [15] Statistical co-learning Statistical-Co [34] ASSEMBLE ASSEMBLE [36] Democratic co-learning Democratic-Co [37] Self-training with editing SETRED [14] Tri-training TriTraining [20] Tri-training with editing DE-TriTraining [”
Data collection and quality challenges in deep learning: a data-centric AI perspective (2021, methodology): “This approach assumes that we can trust the high con-ﬁdence, but there are other techniques including Tri-training [186], Co-learning [185], and Co-training [34] that do not rely on this assumption.”
A survey on semi-supervised learning (2019, methodology): “For example, one can use different hyperparameters for the supervised algorithms (Wang and Zhou 2007; Zhou and Li 2005a), or use different algorithms altogether (Goldman and Zhou 2000; Xu et al. 2012; Zhou and Goldman 2004).”
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples (2007, unknown): “Fortunately, the following derivation inspired by Goldman and Zhou [17] shows that the negative influence caused by such noise could be compensated by augmenting the labeled set with sufficient amount of newly labeled examples under certain conditions.”
Semi-supervised learning by disagreement (2010, unknown): “Later, this idea was extended to involving more learning algorithms [81].”
Combining Committee-Based Semi-Supervised Learning and Active Learning (2010, methodology): “Tri-Training is more applicable than previous CoTraining-Style algorithms because it neither requires multiple views([6,9]) nor does it depend on different supervised learning algorithms[11-12].”
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification (2020, background): “Democratic co-training (Zhou and Goldman, 2004) is a semi-supervised technique, commonly used to create large datasets with noisy labels when provided with a set of diverse models trained in a supervised way.”
MetaFraud: A Meta-Learning Framework for Detecting Financial Fraud (2012, methodology): “Prior studies have effectively used ensemble approaches in concert with semi-supervised learning (Balcan et al. 2005; Ando and Zhang 2007; Zhou and Goldman 2004).”
Co-training by Committee: A New Semi-supervised Learning Framework (2008, methodology): “Single-view, Multi-learner Statistical Co-Learning [10] Multiple classifiers Democratic Co-Learning [20]”