The DOI matches its OpenAlex record; 14 of the 14 references it cites were found in a registry or an index.
Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
Ng, A. Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015435 · Cited by 1,572
Convex multi-task feature learning
Argyriou, A., Evgeniou, T., & Pontil, M. (2008) · Machine Learning · doi:10.1007/s10994-007-5040-8 · Cited by 1,528
Variations in DNA elucidate molecular networks that cause disease
Chen, Y., Zhu, J., Lum, P. Y., Yang, X., Pinto, S., MacNeil, D. J., Zhang, C., Lamb, J., Edwards, S., Sieberts, S. K., et al. (2008) · Nature · doi:10.1038/nature06757 · Cited by 887
Group lasso with overlap and graph lasso
Jacob, L., Obozinski, G., & Vert, J.-P. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553431 · Cited by 833
Consistency of the group Lasso and multiple kernel learning
Bach, F. (2007) · arXiv (Cornell University) · doi:10.48550/arxiv.0707.3390 · Cited by 669
Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E., & Schadt, E. E. (2008) · Nature Genetics · doi:10.1038/ng.167 · Cited by 564
Joint covariate selection and joint subspace selection for multiple classification problems
Obozinski, G., Taskar, B., & Jordan, M. I. (2010) · Statistics and Computing · doi:10.1007/s11222-008-9111-x · Cited by 451
Cited by
A Survey on Multi-Task Learning
Zhang, Y., & Yang, Q. (2017) · arXiv · doi:10.48550/arxiv.1707.08114 · Cited by 2,910
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N., & Kumar, V. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2720168 · Cited by 1,752
Optimization with Sparsity-Inducing Penalties
Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000015 · Cited by 730
Optimization with Sparsity-Inducing Penalties
Bach, F. (2011) · now publishers, Inc. eBooks · doi:10.1561/9781601985118 · Cited by 608
Collaborative Sparse Regression for Hyperspectral Unmixing
Iordache, M.-D., Bioucas-Dias, J. M., & Plaza, A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2240001 · Cited by 485
Metadata
- Authors
- Kim, S., & Xing, E. P.
- Venue
- Figshare
- Publisher
- Figshare (United Kingdom)
- Published
- 2018-06-29
- Pages
- 543-550
- OpenAlex
- W1497745584
- Cited by
- 374
- References
- 14
- FWCI
- 21.37
- Type
- journal article
- Language
- en
- Open access
- green
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- Kim, S., & Xing, E. P. (2018). Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. Figshare. https://doi.org/10.1184/r1/6476414
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