166 citations · 424 across the 44 of their papers we have counts for
6 papers · 1 filter
Topology Adaptive Graph Estimation in High Dimensions
Johannes Lederer, Christian Müller
We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids…
Optimal Two-Step Prediction in Regression
Didier Chételat, Johannes Lederer, Joseph Salmon
High-dimensional prediction typically comprises two steps: variable selection and subsequent least-squares refitting on the selected variables. However, the standard variable selec…
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees
Michaël Chichignoud, Johannes Lederer, Martin Wainwright
We introduce a novel scheme for choosing the regularization parameter in high-dimensional linear regression with Lasso. This scheme, inspired by Lepski's method for bandwidth selec…
Compute Less to Get More: Using ORC to Improve Sparse Filtering
Johannes Lederer, Sergio Guadarrama
Sparse Filtering is a popular feature learning algorithm for image classification pipelines. In this paper, we connect the performance of Sparse Filtering with spectral properties…
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX
Johannes Lederer, Christian Müller
Lasso is a seminal contribution to high-dimensional statistics, but it hinges on a tuning parameter that is difficult to calibrate in practice. A partial remedy for this problem is…
On the Prediction Performance of the Lasso
Arnak S. Dalalyan, Mohamed Hebiri, Johannes Lederer
Although the Lasso has been extensively studied, the relationship between its prediction performance and the correlations of the covariates is not fully understood. In this paper,…