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975. Sparse convex nonparametric least squares via convex optimization
Invited abstract in session WA-41: Convex optimization algorithms, stream Nonsmooth Optimization.
Wednesday, 8:30-10:00Room: 97 (building: 306)
Authors (first author is the speaker)
| 1. | Zhiqiang Liao
|
| Aalto Univerdity |
Abstract
We study the problem of variable selection in convex nonparametric least squares. Whereas the Lasso is a popular technique for simultaneous estimation and variable selection, its performance is unknown in convex regression problems. In this work, we investigate the performance of the Lasso regularized convex nonparametric least squares estimator in a high-dimensional setting and propose an alternative approach based on the unique structure of the subgradients. our proposed estimators perform favorably, while generally leading to sparser models, relative to the other predictive models via the standard Lasso. Further, our estimators can be expressed as solutions to convex optimization problems and are amenable to modern optimization algorithms.
Keywords
- Convex Optimization
- Machine Learning
- Simulation
Status: accepted
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