5 papers
Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications
Anant Mathur, Benoit Liquet, Samuel Muller +1
High-dimensional biomedical studies require models that are simultaneously accurate, sparse, and interpretable, yet exact best subset selection for generalized linear models is com…
Group COMBSS: Group Selection via Continuous Optimization
Anant Mathur, Sarat Moka, Benoit Liquet +1
We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is…
Generalized Linear Models via the Lasso: To Scale or Not to Scale?
Anant Mathur, Sarat Moka, Zdravko Botev
The Lasso regression is a popular regularization method for feature selection in statistics. Prior to computing the Lasso estimator in both linear and generalized linear models, it…
Column Subset Selection and Nyström Approximation via Continuous Optimization
Anant Mathur, Sarat Moka, Zdravko Botev
We propose a continuous optimization algorithm for the Column Subset Selection Problem (CSSP) and Nyström approximation. The CSSP and Nyström method construct low-rank approximatio…
Variance Reduction for Matrix Computations with Applications to Gaussian Processes
Anant Mathur, Sarat Moka, Zdravko Botev
In addition to recent developments in computing speed and memory, methodological advances have contributed to significant gains in the performance of stochastic simulation. In this…