ℓ1-penalization for mixture regression models

Comments on: 1-penalization for mixture regression models

Comments on: 1-penalization for mixture regression models

We would like to wholeheartedly congratulate Professors Städler, Bühlmann and van de Geer for an interesting and important paper on developing the L1 regularization theory and methodology in finite mixture regression (FMR) models. An innovated reparametrization scheme is introduced to ensure equivariance under affine transformations and enhance the performance. Some nonasymptotic oracle inequal...

Sparse Gaussian Process Regression via L1 Penalization

To handle massive data, a variety of sparse Gaussian Process (GP) methods have been proposed to reduce the computational cost. Many of them essentially map the large dataset into a small set of basis points. A common approach to learn these basis points is evidence maximization. Nevertheless, evidence maximization may lead to overfitting and cause a high computational cost. In this paper, we pr...

Comment to “ ` 1 - Penalization for Mixture Regression Models ” by Nicolas Städler , Peter Bühlmann , and Sara van de Geer Gábor

I would like to congratulate the authors for this very interesting contribution. The generalization of `1-penalized linear regression to the “mixture-of-Gaussian-regressions” model raises some very interesting questions both from theoretical and algorithmic points of view and the paper offers a variety of powerful tools to attack both problems. In this comment I would like to mention another di...

Mixture of Regression Models for Precipitation Prediction

One of the key challenges of climate science today is the understanding and prediction of rainfall and other precipitation events [6]. It holds high values for both scientists and policymakers in terms of obtaining novel scientific insight into the processes of precipitation formation, as well as predictions and associated uncertainties which decide future policy decisions based on them. One of...