Johannes Lederer

According to our database, Johannes Lederer authored at least 34 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2.

Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer

CoRR, 2024

Single-Model Attribution of Generative Models Through Final-Layer Inversion.

Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Benchmarking the Fairness of Image Upsampling Methods.

Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer

Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023

DeepMoM: Robust Deep Learning With Median-of-Means.

Shih-Ting Huang, Johannes Lederer

J. Comput. Graph. Stat., January, 2023

Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression.

Mahsa Taheri, Néhémy Lim, Johannes Lederer

IEEE Trans. Inf. Theory, 2023

Affine Invariance in Continuous-Domain Convolutional Neural Networks.

Ali Mohaddes, Johannes Lederer

CoRR, 2023

Set-Membership Inference Attacks using Data Watermarking.

Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer

CoRR, 2023

Single-Model Attribution via Final-Layer Inversion.

Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer

CoRR, 2023

Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming.

Somnath Chakraborty, Johannes Lederer, Rainer von Sachs

CoRR, 2023

The DeepCAR Method: Forecasting Time-Series Data That Have Change Points.

Ayla Jungbluth, Johannes Lederer

CoRR, 2023

2022

Statistical guarantees for sparse deep learning.

Johannes Lederer

CoRR, 2022

Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks.

Mahsa Taheri, Fang Xie, Johannes Lederer

CoRR, 2022

VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering.

Rebecca Marion, Johannes Lederer, Bernadette Govaerts, Rainer von Sachs

CoRR, 2022

Is there a role for statistics in artificial intelligence?

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

Adv. Data Anal. Classif., 2022

Marginal Tail-Adaptive Normalizing Flows.

Mike Laszkiewicz, Johannes Lederer, Asja Fischer

Proceedings of the International Conference on Machine Learning, 2022

2021

Statistical guarantees for regularized neural networks.

Mahsa Taheri, Fang Xie, Johannes Lederer

Neural Networks, 2021

Estimating the Lasso's Effective Noise.

Johannes Lederer, Michael Vogt

J. Mach. Learn. Res., 2021

Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data.

Fang Xie, Johannes Lederer

Entropy, 2021

Tuning-free ridge estimators for high-dimensional generalized linear models.

Shih-Ting Huang, Fang Xie, Johannes Lederer

Comput. Stat. Data Anal., 2021

Copula-Based Normalizing Flows.

Mike Laszkiewicz, Johannes Lederer, Asja Fischer

CoRR, 2021

Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks.

Leni Ven, Johannes Lederer

CoRR, 2021

Targeted Deep Learning: Framework, Methods, and Applications.

Shih-Ting Huang, Johannes Lederer

CoRR, 2021

Activation Functions in Artificial Neural Networks: A Systematic Overview.

Johannes Lederer

CoRR, 2021

False Discovery Rates in Biological Networks.

Lu Yu, Tobias Kaufmann, Johannes Lederer

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery.

Mike Laszkiewicz, Asja Fischer, Johannes Lederer

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks.

Johannes Lederer

CoRR, 2020

Risk Bounds for Robust Deep Learning.

Johannes Lederer

CoRR, 2020

Layer Sparsity in Neural Networks.

Mohamed Hebiri, Johannes Lederer

CoRR, 2020

2016

A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees.

Michael Chichignoud, Johannes Lederer, Martin J. Wainwright

J. Mach. Learn. Res., 2016

Non-convex Global Minimization and False Discovery Rate Control for the TREX.

Jacob Bien, Irina Gaynanova, Johannes Lederer, Christian L. Müller

CoRR, 2016

2015

Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX.

Johannes Lederer, Christian L. Müller

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Compute Less to Get More: Using ORC to Improve Sparse Filtering.

Johannes Lederer, Sergio Guadarrama

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms.

Florentina Bunea, Johannes Lederer, Yiyuan She

IEEE Trans. Inf. Theory, 2014

2013

How Correlations Influence Lasso Prediction.

Mohamed Hebiri, Johannes Lederer

IEEE Trans. Inf. Theory, 2013