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ORCIDJohannes Lederer
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2020 – today
- 2024
[c7]Mike Laszkiewicz
, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. FAccT 2024: 489-517- [c6]Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer:
Single-Model Attribution of Generative Models Through Final-Layer Inversion. ICML 2024 - [i24]Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. CoRR abs/2401.13555 (2024) - [i23]Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2. CoRR abs/2405.14529 (2024) - 2023
- [j10]Shih-Ting Huang, Johannes Lederer:
DeepMoM: Robust Deep Learning With Median-of-Means. J. Comput. Graph. Stat. 32(1): 181-195 (2023) - [j9]Mahsa Taheri, Néhémy Lim, Johannes Lederer:
Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression. IEEE Trans. Inf. Theory 69(1): 316-333 (2023) - [i22]Ayla Jungbluth, Johannes Lederer:
The DeepCAR Method: Forecasting Time-Series Data That Have Change Points. CoRR abs/2302.11241 (2023) - [i21]Somnath Chakraborty, Johannes Lederer, Rainer von Sachs:
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming. CoRR abs/2303.02114 (2023) - [i20]Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer:
Single-Model Attribution via Final-Layer Inversion. CoRR abs/2306.06210 (2023) - [i19]Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer:
Set-Membership Inference Attacks using Data Watermarking. CoRR abs/2307.15067 (2023) - [i18]Ali Mohaddes, Johannes Lederer:
Affine Invariance in Continuous-Domain Convolutional Neural Networks. CoRR abs/2311.09245 (2023) - 2022
- [j8]Sarah Friedrich, Gerd Antes, Sigrid Behr, Harald Binder, Werner Brannath, Florian Dumpert, Katja Ickstadt, Hans A. Kestler, Johannes Lederer, Heinz Leitgöb, Markus Pauly, Ansgar Steland, Adalbert F. X. Wilhelm, Tim Friede:
Is there a role for statistics in artificial intelligence? Adv. Data Anal. Classif. 16(4): 823-846 (2022) - [c5]Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
Marginal Tail-Adaptive Normalizing Flows. ICML 2022: 12020-12048 - [i17]Rebecca Marion, Johannes Lederer, Bernadette Govaerts, Rainer von Sachs:
VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering. CoRR abs/2202.00975 (2022) - [i16]Mahsa Taheri, Fang Xie, Johannes Lederer:
Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks. CoRR abs/2205.04491 (2022) - [i15]Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
Marginal Tail-Adaptive Normalizing Flows. CoRR abs/2206.10311 (2022) - [i14]Johannes Lederer:
Statistical guarantees for sparse deep learning. CoRR abs/2212.05427 (2022) - 2021
- [j7]Shih-Ting Huang, Fang Xie, Johannes Lederer:
Tuning-free ridge estimators for high-dimensional generalized linear models. Comput. Stat. Data Anal. 159: 107205 (2021) - [j6]Fang Xie, Johannes Lederer:
Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data. Entropy 23(2): 230 (2021) - [j5]Johannes Lederer, Michael Vogt:
Estimating the Lasso's Effective Noise. J. Mach. Learn. Res. 22: 276:1-276:32 (2021) - [j4]Mahsa Taheri, Fang Xie, Johannes Lederer:
Statistical guarantees for regularized neural networks. Neural Networks 142: 148-161 (2021) - [c4]Lu Yu, Tobias Kaufmann, Johannes Lederer:
False Discovery Rates in Biological Networks. AISTATS 2021: 163-171 - [c3]Mike Laszkiewicz, Asja Fischer, Johannes Lederer:
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. AISTATS 2021: 1864-1872 - [i13]Johannes Lederer:
Activation Functions in Artificial Neural Networks: A Systematic Overview. CoRR abs/2101.09957 (2021) - [i12]Shih-Ting Huang, Johannes Lederer:
DeepMoM: Robust Deep Learning With Median-of-Means. CoRR abs/2105.14035 (2021) - [i11]Shih-Ting Huang, Johannes Lederer:
Targeted Deep Learning: Framework, Methods, and Applications. CoRR abs/2105.14052 (2021) - [i10]Leni Ven, Johannes Lederer:
Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks. CoRR abs/2106.02260 (2021) - [i9]Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
Copula-Based Normalizing Flows. CoRR abs/2107.07352 (2021) - 2020
- [i8]Mike Laszkiewicz, Asja Fischer, Johannes Lederer:
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. CoRR abs/2005.00466 (2020) - [i7]Mahsa Taheri, Fang Xie, Johannes Lederer:
Statistical Guarantees for Regularized Neural Networks. CoRR abs/2006.00294 (2020) - [i6]Mohamed Hebiri, Johannes Lederer:
Layer Sparsity in Neural Networks. CoRR abs/2006.15604 (2020) - [i5]Johannes Lederer:
Risk Bounds for Robust Deep Learning. CoRR abs/2009.06202 (2020) - [i4]Sarah Friedrich, Gerd Antes, Sigrid Behr, Harald Binder, Werner Brannath, Florian Dumpert, Katja Ickstadt, Hans A. Kestler, Johannes Lederer, Heinz Leitgöb, Markus Pauly, Ansgar Steland, Adalbert F. X. Wilhelm, Tim Friede:
Is there a role for statistics in artificial intelligence? CoRR abs/2009.09070 (2020) - [i3]Johannes Lederer:
No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks. CoRR abs/2010.00885 (2020)
2010 – 2019
- 2016
- [j3]Michael Chichignoud, Johannes Lederer, Martin J. Wainwright:
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees. J. Mach. Learn. Res. 17: 231:1-231:20 (2016) - [i2]Jacob Bien, Irina Gaynanova, Johannes Lederer, Christian L. Müller:
Non-convex Global Minimization and False Discovery Rate Control for the TREX. CoRR abs/1604.06815 (2016) - 2015
- [c2]Johannes Lederer, Christian L. Müller:
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX. AAAI 2015: 2729-2735 - [c1]Johannes Lederer, Sergio Guadarrama:
Compute Less to Get More: Using ORC to Improve Sparse Filtering. AAAI 2015: 3797-3803 - 2014
- [j2]Florentina Bunea, Johannes Lederer, Yiyuan She:
The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms. IEEE Trans. Inf. Theory 60(2): 1313-1325 (2014) - [i1]Johannes Lederer, Sergio Guadarrama:
Compute Less to Get More: Using ORC to Improve Sparse Filtering. CoRR abs/1409.4689 (2014) - 2013
- [j1]Mohamed Hebiri, Johannes Lederer:
How Correlations Influence Lasso Prediction. IEEE Trans. Inf. Theory 59(3): 1846-1854 (2013)
Coauthor Index
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last updated on 2024-09-13 01:41 CEST by the dblp team
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