166 citations · 424 across the 44 of their papers we have counts for
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Tuning parameter calibration for -regularized logistic regression
Wei Li, Johannes Lederer
Feature selection is a standard approach to understanding and modeling high-dimensional classification data, but the corresponding statistical methods hinge on tuning parameters th…
Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression
Mahsa Taheri, Néhémy Lim, Johannes Lederer
Modern technologies are generating ever-increasing amounts of data. Making use of these data requires methods that are both statistically sound and computationally efficient. Typic…
Graphical Models for Discrete and Continuous Data
Rui Zhuang, Noah Simon, Johannes Lederer
We introduce a general framework for undirected graphical models. It generalizes Gaussian graphical models to a wide range of continuous, discrete, and combinations of different ty…
Oracle Inequalities for High-dimensional Prediction
Johannes Lederer, Lu Yu, Irina Gaynanova
The abundance of high-dimensional data in the modern sciences has generated tremendous interest in penalized estimators such as the lasso, scaled lasso, square-root lasso, elastic…
Non-convex Global Minimization and False Discovery Rate Control for the TREX
Jacob Bien, Irina Gaynanova, Johannes Lederer +1
The TREX is a recently introduced method for performing sparse high-dimensional regression. Despite its statistical promise as an alternative to the lasso, square-root lasso, and s…