Fused Lasso

Fused Lasso is a regularized regression method that extends the Lasso by imposing sparsity on both the coefficients and their successive differences, thereby encouraging piecewise constant solutions in high-dimensional data.

157

Publications

14K

Citations

426

Authors

233

Institutions

Publications per year

2017–2026

53

Authors

426

  1. RJ
    Ryan J. Tibshirani

    Carnegie Mellon University, United States

    8 publications · 280 citations

    8

    h-index

  2. JY
    Jieping Ye

    Arizona State University, United States

    6 publications · 616 citations

    6

    h-index

  3. JS
    James Sharpnack

    University of California, Davis, United States

    6 publications · 200 citations

    6

    h-index

  4. JL
    Johan Lim

    Seoul National University, South Korea

    5 publications · 146 citations

    5

    h-index

  5. DY
    Donghyeon Yu

    Harbin Institute of Technology, China

    5 publications · 146 citations

    5

    h-index

1–5 of 426

Venues

  1. arXiv (Cornell University)

    Repository · 18 publications · 570 citations

    15

    h-index

  2. Journal of Computational and Graphical Statistics

    JCGS · 7 publications · 557 citations

    7

    h-index

  3. 4

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 441 citations

    4

    h-index

  5. 4

    h-index

1–5