Johannes Lederer

47 papers A* 5A 3Journal 38Unranked 1

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2. WACV Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer
2025	J	`jnl`	Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning. Trans. Mach. Learn. Res. Ali Mohaddes, Johannes Lederer
2025	A*	`conf`	How many samples are needed to train a deep neural network? ICLR Pegah Golestaneh, Mahsa Taheri, Johannes Lederer
2025	J	`jnl`	Non-asymptotic error bounds for probability flow ODEs under weak log-concavity. CoRR Gitte Kremling, Francesco Iafrate, Mahsa Taheri, Johannes Lederer
2025	J	`jnl`	Regularization can make diffusion models more efficient. CoRR Mahsa Taheri, Johannes Lederer
2025	J	`jnl`	Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks. Trans. Mach. Learn. Res. Mahsa Taheri, Fang Xie, Johannes Lederer
2024	J	`jnl`	AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2. CoRR Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer
2024	—	`conf`	Benchmarking the Fairness of Image Upsampling Methods. FAccT Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
2024	J	`jnl`	Benchmarking the Fairness of Image Upsampling Methods. CoRR Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
2024	A*	`conf`	Single-Model Attribution of Generative Models Through Final-Layer Inversion. ICML Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer
2023	J	`jnl`	Affine Invariance in Continuous-Domain Convolutional Neural Networks. CoRR Ali Mohaddes, Johannes Lederer
2023	J	`jnl`	Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression. IEEE Trans. Inf. Theory Mahsa Taheri, Néhémy Lim, Johannes Lederer
2023	J	`jnl`	DeepMoM: Robust Deep Learning With Median-of-Means. J. Comput. Graph. Stat. Shih-Ting Huang, Johannes Lederer
2023	J	`jnl`	Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming. CoRR Somnath Chakraborty, Johannes Lederer, Rainer von Sachs
2023	J	`jnl`	Set-Membership Inference Attacks using Data Watermarking. CoRR Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer
2023	J	`jnl`	Single-Model Attribution via Final-Layer Inversion. CoRR Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer
2023	J	`jnl`	The DeepCAR Method: Forecasting Time-Series Data That Have Change Points. CoRR Ayla Jungbluth, Johannes Lederer
2022	J	`jnl`	Is there a role for statistics in artificial intelligence? Adv. Data Anal. Classif. 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
2022	A*	`conf`	Marginal Tail-Adaptive Normalizing Flows. ICML Mike Laszkiewicz, Johannes Lederer, Asja Fischer
2022	J	`jnl`	Marginal Tail-Adaptive Normalizing Flows. CoRR Mike Laszkiewicz, Johannes Lederer, Asja Fischer
2022	J	`jnl`	Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks. CoRR Mahsa Taheri, Fang Xie, Johannes Lederer
2022	J	`jnl`	Statistical guarantees for sparse deep learning. CoRR Johannes Lederer
2022	J	`jnl`	VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering. CoRR Rebecca Marion, Johannes Lederer, Bernadette Govaerts, Rainer von Sachs
2021	J	`jnl`	Activation Functions in Artificial Neural Networks: A Systematic Overview. CoRR Johannes Lederer
2021	J	`jnl`	Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data. Entropy Fang Xie, Johannes Lederer
2021	J	`jnl`	Copula-Based Normalizing Flows. CoRR Mike Laszkiewicz, Johannes Lederer, Asja Fischer
2021	J	`jnl`	DeepMoM: Robust Deep Learning With Median-of-Means. CoRR Shih-Ting Huang, Johannes Lederer
2021	J	`jnl`	Estimating the Lasso's Effective Noise. J. Mach. Learn. Res. Johannes Lederer, Michael Vogt
2021	A	`conf`	False Discovery Rates in Biological Networks. AISTATS Lu Yu, Tobias Kaufmann, Johannes Lederer
2021	J	`jnl`	Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks. CoRR Leni Aniva, Johannes Lederer
2021	J	`jnl`	Statistical guarantees for regularized neural networks. Neural Networks Mahsa Taheri, Fang Xie, Johannes Lederer
2021	J	`jnl`	Targeted Deep Learning: Framework, Methods, and Applications. CoRR Shih-Ting Huang, Johannes Lederer
2021	A	`conf`	Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. AISTATS Mike Laszkiewicz, Asja Fischer, Johannes Lederer
2021	J	`jnl`	Tuning-free ridge estimators for high-dimensional generalized linear models. Comput. Stat. Data Anal. Shih-Ting Huang, Fang Xie, Johannes Lederer
2020	J	`jnl`	Is there a role for statistics in artificial intelligence? CoRR 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
2020	J	`jnl`	Layer Sparsity in Neural Networks. CoRR Mohamed Hebiri, Johannes Lederer
2020	J	`jnl`	No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks. CoRR Johannes Lederer
2020	J	`jnl`	Risk Bounds for Robust Deep Learning. CoRR Johannes Lederer
2020	J	`jnl`	Statistical Guarantees for Regularized Neural Networks. CoRR Mahsa Taheri, Fang Xie, Johannes Lederer
2020	J	`jnl`	Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. CoRR Mike Laszkiewicz, Asja Fischer, Johannes Lederer
2016	J	`jnl`	A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees. J. Mach. Learn. Res. Michael Chichignoud, Johannes Lederer, Martin J. Wainwright
2016	J	`jnl`	Non-convex Global Minimization and False Discovery Rate Control for the TREX. CoRR Jacob Bien, Irina Gaynanova, Johannes Lederer, Christian L. Müller
2015	A*	`conf`	Compute Less to Get More: Using ORC to Improve Sparse Filtering. AAAI Johannes Lederer, Sergio Guadarrama
2015	A*	`conf`	Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX. AAAI Johannes Lederer, Christian L. Müller
2014	J	`jnl`	Compute Less to Get More: Using ORC to Improve Sparse Filtering. CoRR Johannes Lederer, Sergio Guadarrama
2014	J	`jnl`	The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms. IEEE Trans. Inf. Theory Florentina Bunea, Johannes Lederer, Yiyuan She
2013	J	`jnl`	How Correlations Influence Lasso Prediction. IEEE Trans. Inf. Theory Mohamed Hebiri, Johannes Lederer