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Lederer, Johannes
13 publications
WACV
2025
AnomalyDINO: Boosting Patch-Based Few-Shot Anomaly Detection with DINOv2
Simon Damm
,
Mike Laszkiewicz
,
Johannes Lederer
,
Asja Fischer
TMLR
2025
Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning
Ali Mohaddes
,
Johannes Lederer
ICLR
2025
How Many Samples Are Needed to Train a Deep Neural Network?
Pegah Golestaneh
,
Mahsa Taheri
,
Johannes Lederer
TMLR
2025
Statistical Guarantees for Approximate Stationary Points of Shallow Neural Networks
Mahsa Taheri
,
Fang Xie
,
Johannes Lederer
ICML
2024
Single-Model Attribution of Generative Models Through Final-Layer Inversion
Mike Laszkiewicz
,
Jonas Ricker
,
Johannes Lederer
,
Asja Fischer
ICML
2022
Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz
,
Johannes Lederer
,
Asja Fischer
AISTATS
2021
False Discovery Rates in Biological Networks
Lu Yu
,
Tobias Kaufmann
,
Johannes Lederer
AISTATS
2021
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
Mike Laszkiewicz
,
Asja Fischer
,
Johannes Lederer
ICMLW
2021
Copula-Based Normalizing Flows
Mike Laszkiewicz
,
Johannes Lederer
,
Asja Fischer
JMLR
2021
Estimating the Lasso's Effective Noise
Johannes Lederer
,
Michael Vogt
JMLR
2016
A Practical Scheme and Fast Algorithm to Tune the Lasso with Optimality Guarantees
Michael Chichignoud
,
Johannes Lederer
,
Martin J. Wainwright
AAAI
2015
Compute Less to Get More: Using ORC to Improve Sparse Filtering
Johannes Lederer
,
Sergio Guadarrama
AAAI
2015
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions with the TREX
Johannes Lederer
,
Christian L. Müller