2 citations · 3 across the 18 of their papers we have counts for
8 papers · 1 filter
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…
Continuous Optimization for Offline Change Point Detection and Estimation
Hans Reimann, Sarat Moka, Georgy Sofronov
This work explores use of novel advances in best subset selection for regression modelling via continuous optimization for offline change point detection and estimation in univaria…
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…
Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization
Benoit Liquet, Sarat Moka, Samuel Muller
The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually m…
Spatial Autoregressive Model on a Dirichlet Distribution
Teo Nguyen, Sarat Moka, Kerrie Mengersen +1
Compositional data find broad application across diverse fields due to their efficacy in representing proportions or percentages of various components within a whole. Spatial depen…
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…
COMBSS: Best Subset Selection via Continuous Optimization
Sarat Moka, Benoit Liquet, Houying Zhu +1
The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly chall…