Efficient co-regularised least squares regression

By Ulf Brefeld, Thomas Gärtner, Tobias Scheffer, Stefan Wrobel. 2006

179 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Fast Learning from Distributed Datasets without Entity Matching (2016, background): “[Brefeld et al., 2006] presents a multi-view distributed algorithm with co-regularization; although it requires matches for all unlabelled examples.”
On causal and anticausal learning (2012, methodology): “To this end, we consider the co-regularized least squares regression (co-RLSR) algorithm, compared to regular RLSR on 32 real-world data sets by Brefeld et al. (2006) (two of which are identical, so 31 data sets were considered).”
A Survey on Multi-view Learning (2013, methodology): “Inspired by the co-regularization algorithm, Brefeld et al. (2006) proposed a co-regression algorithm.”
Semi-supervised Learning in Causal and Anticausal Settings (2013, background): “[1] (two of which are identical, so 31 datasets are considered).”
An Information Theoretic Framework for Multi-view Learning (2008, methodology): “, 2005, Brefeld et al., 2006]. A related algorithm is the two view SVM-2K algorithm of Farquhar et al. [2005]. These class of algorithms all have demonstrated empirical successes.”
Semi-Supervised Dimensionality Reduction (2007, background): “semi-supervised classification [5, 19], semi-supervised regression [6, 20], and semi-supervised clustering [2, 16].”
Distributed Distillation for On-Device Learning (2020, methodology): “While our algorithm is a distributed extension of knowledge distillation, it also can be thought of as an extension of co-regularization [32,33].”
Multi-view Regression Via Canonical Correlation Analysis (2007, background): “A characteristic of many of the multi-view learning algorithms [Yarowsky, 1995, Blum and Mitchell, 1998, Farquhar et al., 2005, Sindhwani et al., 2005, Brefeld et al., 2006] is to force agreement between the predictors, based on either view.”
Linear Algorithms for Online Multitask Classification (2010, background): “In such a case, the correlation among tasks is naturally measured through the task margins u> i x (or views) —see, e.g., the previous work of [ 6 , 27] for a similar approach in the context of semi-supervised learning.”
Anomaly Detection in Vertically Partitioned Data by Distributed Core Vector Machines (2013, methodology): “[4] use co-regularisation and block coordinate descent for least squares regression.”
Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, unknown): “[17] proposed multi-view semi-supervised regression techniques.”