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Johannes Lederer
Johannes Lederer
Professor of Data-Driven Methods, University of Hamburg
Verified email at uni-hamburg.de - Homepage
Title
Cited by
Cited by
Year
On the prediction performance of the lasso
AS Dalalyan, M Hebiri, J Lederer
2192017
How correlations influence lasso prediction
M Hebiri, J Lederer
IEEE Transactions on Information Theory 59 (3), 1846-1854, 2012
1502012
Activation functions in artificial neural networks: A systematic overview
J Lederer
arXiv preprint arXiv:2101.09957, 2021
1332021
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
1212013
The Bernstein–Orlicz norm and deviation inequalities
S van de Geer, J Lederer
Probability theory and related fields 157 (1), 225-250, 2013
1042013
Is there a role for statistics in artificial intelligence?
S Friedrich, G Antes, S Behr, H Binder, W Brannath, F Dumpert, K Ickstadt, ...
Advances in Data Analysis and Classification 16 (4), 823-846, 2022
832022
A practical scheme and fast algorithm to tune the lasso with optimality guarantees
M Chichignoud, J Lederer, MJ Wainwright
Journal of Machine Learning Research 17 (229), 1-20, 2016
78*2016
Don't fall for tuning parameters: tuning-free variable selection in high dimensions with the TREX
J Lederer, C Müller
Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015
782015
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
712013
Inference for high-dimensional instrumental variables regression
D Gold, J Lederer, J Tao
Journal of Econometrics 217 (1), 79-111, 2020
562020
Fundamentals of High-Dimensional Statistics
J Lederer
Springer International Publishing, Cham, Switzerland, 2022
55*2022
Statistical guarantees for regularized neural networks
M Taheri, F Xie, J Lederer
Neural networks 142, 148-161, 2021
452021
Oracle inequalities for high-dimensional prediction
J Lederer, L Yu, I Gaynanova
402019
New concentration inequalities for suprema of empirical processes
J Lederer, S Van De Geer
382014
Trust, but verify: benefits and pitfalls of least-squares refitting in high dimensions
J Lederer
arXiv preprint arXiv:1306.0113, 2013
342013
Risk bounds for robust deep learning
J Lederer
arXiv preprint arXiv:2009.06202, 2020
262020
Integrating additional knowledge into the estimation of graphical models
Y Bu, J Lederer
The international journal of biostatistics 18 (1), 1-17, 2022
252022
Estimating the Lasso's effective noise
J Lederer, M Vogt
Journal of Machine Learning Research 22 (276), 1-32, 2021
252021
Balancing statistical and computational precision: A general theory and applications to sparse regression
M Taheri, N Lim, J Lederer
IEEE Transactions on Information Theory 69 (1), 316-333, 2022
24*2022
Tuning parameter calibration for ℓ1-regularized logistic regression
W Li, J Lederer
Journal of Statistical Planning and Inference 202, 80-98, 2019
242019
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