Johannes Lederer

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 37

Top Topics

Sparse Regression

Top Venues

IEEE Trans. Inf. Theory

J. Mach. Learn. Res.

Publications

Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
Benchmarking the Fairness of Image Upsampling Methods. CoRR (2024)
Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
Benchmarking the Fairness of Image Upsampling Methods. FAccT (2024)
Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2. CoRR (2024)
Ali Mohaddes, Johannes Lederer
Affine Invariance in Continuous-Domain Convolutional Neural Networks. CoRR (2023)
Somnath Chakraborty, Johannes Lederer, Rainer von Sachs
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming. CoRR (2023)
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) (2023)
Ayla Jungbluth, Johannes Lederer
The DeepCAR Method: Forecasting Time-Series Data That Have Change Points. CoRR (2023)
Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer
Single-Model Attribution via Final-Layer Inversion. CoRR (2023)
Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer
Set-Membership Inference Attacks using Data Watermarking. CoRR (2023)
Shih-Ting Huang, Johannes Lederer
DeepMoM: Robust Deep Learning With Median-of-Means. J. Comput. Graph. Stat. 32 (1) (2023)
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Marginal Tail-Adaptive Normalizing Flows. ICML (2022)
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) (2022)
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Marginal Tail-Adaptive Normalizing Flows. CoRR (2022)
Mahsa Taheri, Fang Xie, Johannes Lederer
Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks. CoRR (2022)
Rebecca Marion, Johannes Lederer, Bernadette Govaerts, Rainer von Sachs
VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering. CoRR (2022)
Johannes Lederer
Statistical guarantees for sparse deep learning. CoRR (2022)
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Copula-Based Normalizing Flows. CoRR (2021)
Shih-Ting Huang, Johannes Lederer
Targeted Deep Learning: Framework, Methods, and Applications. CoRR (2021)
Mahsa Taheri, Fang Xie, Johannes Lederer
Statistical guarantees for regularized neural networks. Neural Networks 142 (2021)
Shih-Ting Huang, Fang Xie, Johannes Lederer
Tuning-free ridge estimators for high-dimensional generalized linear models. Comput. Stat. Data Anal. 159 (2021)
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. AISTATS (2021)
Johannes Lederer, Michael Vogt
Estimating the Lasso's Effective Noise. J. Mach. Learn. Res. 22 (2021)
Shih-Ting Huang, Johannes Lederer
DeepMoM: Robust Deep Learning With Median-of-Means. CoRR (2021)
Lu Yu, Tobias Kaufmann, Johannes Lederer
False Discovery Rates in Biological Networks. AISTATS (2021)
Johannes Lederer
Activation Functions in Artificial Neural Networks: A Systematic Overview. CoRR (2021)
Fang Xie, Johannes Lederer
Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data. Entropy 23 (2) (2021)
Leni Ven, Johannes Lederer
Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks. CoRR (2021)
Mahsa Taheri, Fang Xie, Johannes Lederer
Statistical Guarantees for Regularized Neural Networks. CoRR (2020)
Mike Laszkiewicz, Asja Fischer, Johannes Lederer
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. CoRR (2020)
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 (2020)
Mohamed Hebiri, Johannes Lederer
Layer Sparsity in Neural Networks. CoRR (2020)
Johannes Lederer
No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks. CoRR (2020)
Johannes Lederer
Risk Bounds for Robust Deep Learning. CoRR (2020)
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 (2016)
Jacob Bien, Irina Gaynanova, Johannes Lederer, Christian L. Müller
Non-convex Global Minimization and False Discovery Rate Control for the TREX. CoRR (2016)
Johannes Lederer, Sergio Guadarrama
Compute Less to Get More: Using ORC to Improve Sparse Filtering. AAAI (2015)
Johannes Lederer, Christian L. Müller
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX. AAAI (2015)
Johannes Lederer, Sergio Guadarrama
Compute Less to Get More: Using ORC to Improve Sparse Filtering. CoRR (2014)
Florentina Bunea, Johannes Lederer, Yiyuan She
The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms. IEEE Trans. Inf. Theory 60 (2) (2014)
Mohamed Hebiri, Johannes Lederer
How Correlations Influence Lasso Prediction. IEEE Trans. Inf. Theory 59 (3) (2013)