Descent methods for tuning parameter refinement

Alexander Lorbert, Peter J. Ramadge

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


This paper addresses multidimensional tuning parameter selection in the context of "train-validate-test" and K-fold cross validation. A coarse grid search over tuning parameter space is used to initialize a descent method which then jointly optimizes over variables and tuning parameters. We study four regularized regression methods and develop the update equations for the corresponding descent algorithms. Experiments on both simulated and real-world datasets show that the method results in significant tuning parameter refinement.

Original languageEnglish (US)
Pages (from-to)469-476
Number of pages8
JournalJournal of Machine Learning Research
StatePublished - 2010
Event13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy
Duration: May 13 2010May 15 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability


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