Corpus ID: 1842866

GAP Safe Screening Rules for Sparse-Group Lasso

@inproceedings{Ndiaye2016GAPSS,
  title={GAP Safe Screening Rules for Sparse-Group Lasso},
  author={Eug{\`e}ne Ndiaye and Olivier Fercoq and Alexandre Gramfort and Joseph Salmon},
  booktitle={NIPS},
  year={2016}
}
  • Eugène Ndiaye, Olivier Fercoq, +1 author Joseph Salmon
  • Published in NIPS 2016
  • Computer Science, Mathematics
  • In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more refined structures than pure sparsity, such as for instance group sparsity. Sparse-Group Lasso has recently been introduced in the context of linear regression to enforce sparsity both at the feature level and at the group level. We adapt to the case of Sparse-Group Lasso recent safe screening rules that discard early in… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 31 REFERENCES

    A dynamic screening principle for the Lasso

    VIEW 13 EXCERPTS
    HIGHLY INFLUENTIAL

    Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets

    VIEW 7 EXCERPTS
    HIGHLY INFLUENTIAL

    Safe Feature Elimination in Sparse Supervised Learning

    VIEW 9 EXCERPTS
    HIGHLY INFLUENTIAL

    On a new norm for data fitting and optimization problems

    • O. Burdakov, B. Merkulov
    • 2001
    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Convex multi-task feature learning

    VIEW 9 EXCERPTS
    HIGHLY INFLUENTIAL

    Lectures on Convex Optimization

    VIEW 1 EXCERPT
    HIGHLY INFLUENTIAL

    Dynamic Screening: Accelerating First-Order Algorithms for the Lasso and Group-Lasso

    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL