| On the prediction performance of the lasso AS Dalalyan, M Hebiri, J Lederer | 197 | 2017 |
| How correlations influence lasso prediction M Hebiri, J Lederer IEEE Transactions on Information Theory 59 (3), 1846-1854, 2012 | 141 | 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 | 117 | 2013 |
| The Bernstein–Orlicz norm and deviation inequalities S van de Geer, J Lederer Probability theory and related fields 157 (1), 225-250, 2013 | 98 | 2013 |
| Activation functions in artificial neural networks: A systematic overview J Lederer arXiv preprint arXiv:2101.09957, 2021 | 76 | 2021 |
| 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 | 75 | 2015 |
| 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 | 70* | 2016 |
| 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 | 68 | 2013 |
| 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 | 56 | 2022 |
| Inference for high-dimensional instrumental variables regression D Gold, J Lederer, J Tao Journal of Econometrics 217 (1), 79-111, 2020 | 46 | 2020 |
| Oracle inequalities for high-dimensional prediction J Lederer, L Yu, I Gaynanova | 40 | 2019 |
| New concentration inequalities for suprema of empirical processes J Lederer, S Van De Geer Bernoulli, 2020-2038, 2014 | 39 | 2014 |
| Trust, but verify: benefits and pitfalls of least-squares refitting in high dimensions J Lederer arXiv preprint arXiv:1306.0113, 2013 | 34 | 2013 |
| Statistical guarantees for regularized neural networks M Taheri, F Xie, J Lederer Neural Networks 142, 148-161, 2021 | 31 | 2021 |
| Fundamentals of High-Dimensional Statistics J Lederer Springer International Publishing, Cham, Switzerland, 2022 | 30* | 2022 |
| 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 | 23 | 2018 |
| Integrating additional knowledge into the estimation of graphical models Y Bu, J Lederer The international journal of biostatistics 18 (1), 1-17, 2022 | 21 | 2022 |
| Risk bounds for robust deep learning J Lederer arXiv preprint arXiv:2009.06202, 2020 | 21 | 2020 |
| Optimal two-step prediction in regression D Chételat, J Lederer, J Salmon | 21 | 2017 |
| Tuning parameter calibration for ℓ1-regularized logistic regression W Li, J Lederer Journal of Statistical Planning and Inference 202, 80-98, 2019 | 20 | 2019 |
