Johannes Lederer
Johannes Lederer
Professor of Mathematical Statistics, Ruhr-University Bochum
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On the prediction performance of the lasso
AS Dalalyan, M Hebiri, J Lederer
Bernoulli 23 (1), 552-581, 2017
How correlations influence lasso prediction
M Hebiri, J Lederer
IEEE Transactions on Information Theory 59 (3), 1846-1854, 2012
The group square-root lasso: Theoretical properties and fast algorithms
F Bunea, J Lederer, Y She
IEEE Transactions on Information Theory 60 (2), 1313-1325, 2013
The Bernstein–Orlicz norm and deviation inequalities
S van de Geer, J Lederer
Probability theory and related fields 157 (1-2), 225-250, 2013
The Lasso, correlated design, and improved oracle inequalities
S van de Geer, J Lederer
From Probability to Statistics and Back: High-Dimensional Models and …, 2013
Don't fall for tuning parameters: tuning-free variable selection in high dimensions with the TREX
J Lederer, C Müller
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
A practical scheme and fast algorithm to tune the lasso with optimality guarantees
M Chichignoud, J Lederer, MJ Wainwright
The Journal of Machine Learning Research 17 (1), 8162-8181, 2016
New concentration inequalities for suprema of empirical processes
J Lederer, S Van De Geer
Bernoulli 20 (4), 2020-2038, 2014
Trust, but verify: benefits and pitfalls of least-squares refitting in high dimensions
J Lederer
arXiv preprint arXiv:1306.0113, 2013
A robust, adaptive M-estimator for pointwise estimation in heteroscedastic regression
M Chichignoud, J Lederer
Bernoulli 20 (3), 1560-1599, 2014
Oracle inequalities for high-dimensional prediction
J Lederer, L Yu, I Gaynanova
Bernoulli 25 (2), 1225-1255, 2019
Integrating additional knowledge into estimation of graphical models
Y Bu, J Lederer
arXiv preprint arXiv:1704.02739, 2017
Optimal two-step prediction in regression
D Chételat, J Lederer, J Salmon
Electronic Journal of Statistics 11 (1), 2519-2546, 2017
Maximum regularized likelihood estimators: A general prediction theory and applications
R Zhuang, J Lederer
Stat 7 (1), e186, 2018
Non-convex global minimization and false discovery rate control for the TREX
J Bien, I Gaynanova, J Lederer, CL Müller
Journal of Computational and Graphical Statistics 27 (1), 23-33, 2018
Graphical models for discrete and continuous data
R Zhuang, N Simon, J Lederer
arXiv preprint arXiv:1609.05551, 2016
Topology adaptive graph estimation in high dimensions
J Lederer, C Müller
arXiv preprint arXiv:1410.7279, 2014
Inference for high-dimensional instrumental variables regression
D Gold, J Lederer, J Tao
Journal of Econometrics, 2019
Compute less to get more: using ORC to improve sparse filtering
J Lederer, S Guadarrama
Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015
Balancing Statistical and Computational Precision and Applications to Penalized Linear Regression with Group Sparsity
M Taheri, N Lim, J Lederer
arXiv preprint arXiv:1609.07195, 2016
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