Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers is a research paper published in Foundations and Trends® in Machine Learning (2010). On theSindex it has a DataRank of 1.5. It has been cited 15,819 times.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
1.5
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →FWCI
435.84
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Inference of Multiscale Gaussian Graphical Models
Additional file 1 of Multi-task learning sparse group lasso: a method for quantifying antigenicity of influenza A(H1N1) virus using mutations and variations in glycosylation of Hemagglutinin
Additional file 1 of Multi-task learning sparse group lasso: a method for quantifying antigenicity of influenza A(H1N1) virus using mutations and variations in glycosylation of Hemagglutinin
Additional file 1 of Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets
Additional file 1 of Coupled mixed model for joint genetic analysis of complex disorders with two independently collected data sets
Additional file 10 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 10 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 11 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 11 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 12 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 12 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 13 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 13 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 2 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 2 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 3 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 3 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 4 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 4 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
Additional file 5 of A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations