On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About its Nonsmooth Loss Function

Xingguo Li, Haoming Jiang, Jarvis D. Haupt, Raman Arora, Han Liu 0001, Mingyi Hong, Tuo Zhao. On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About its Nonsmooth Loss Function. In Amir Globerson, Ricardo Silva, editors, Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019. pages 13, AUAI Press, 2019. [doi]

@inproceedings{LiJHA0HZ19,
  title = {On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About its Nonsmooth Loss Function},
  author = {Xingguo Li and Haoming Jiang and Jarvis D. Haupt and Raman Arora and Han Liu 0001 and Mingyi Hong and Tuo Zhao},
  year = {2019},
  url = {http://auai.org/uai2019/proceedings/papers/13.pdf},
  researchr = {https://researchr.org/publication/LiJHA0HZ19},
  cites = {0},
  citedby = {0},
  pages = {13},
  booktitle = {Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019},
  editor = {Amir Globerson and Ricardo Silva},
  publisher = {AUAI Press},
}