166 citations · 424 across the 44 of their papers we have counts for
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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…
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…