Johannes Lederer

Orcid: 0000-0002-5369-3053

According to our database1, Johannes Lederer authored at least 34 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

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Bibliography

2024
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2.
CoRR, 2024

Single-Model Attribution of Generative Models Through Final-Layer Inversion.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Benchmarking the Fairness of Image Upsampling Methods.
Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

2023
DeepMoM: Robust Deep Learning With Median-of-Means.
J. Comput. Graph. Stat., January, 2023

Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression.
IEEE Trans. Inf. Theory, 2023

Affine Invariance in Continuous-Domain Convolutional Neural Networks.
CoRR, 2023

Set-Membership Inference Attacks using Data Watermarking.
CoRR, 2023

Single-Model Attribution via Final-Layer Inversion.
CoRR, 2023

Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming.
CoRR, 2023

The DeepCAR Method: Forecasting Time-Series Data That Have Change Points.
CoRR, 2023

2022
Statistical guarantees for sparse deep learning.
CoRR, 2022

Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks.
CoRR, 2022

VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering.
CoRR, 2022

Is there a role for statistics in artificial intelligence?
Adv. Data Anal. Classif., 2022

Marginal Tail-Adaptive Normalizing Flows.
Proceedings of the International Conference on Machine Learning, 2022

2021
Statistical guarantees for regularized neural networks.
Neural Networks, 2021

Estimating the Lasso's Effective Noise.
J. Mach. Learn. Res., 2021

Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data.
Entropy, 2021

Tuning-free ridge estimators for high-dimensional generalized linear models.
Comput. Stat. Data Anal., 2021

Copula-Based Normalizing Flows.
CoRR, 2021

Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks.
CoRR, 2021

Targeted Deep Learning: Framework, Methods, and Applications.
CoRR, 2021

Activation Functions in Artificial Neural Networks: A Systematic Overview.
CoRR, 2021

False Discovery Rates in Biological Networks.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery.
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.
CoRR, 2020

Risk Bounds for Robust Deep Learning.
CoRR, 2020

Layer Sparsity in Neural Networks.
CoRR, 2020

2016
A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees.
J. Mach. Learn. Res., 2016

Non-convex Global Minimization and False Discovery Rate Control for the TREX.
CoRR, 2016

2015
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

Compute Less to Get More: Using ORC to Improve Sparse Filtering.
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014
The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms.
IEEE Trans. Inf. Theory, 2014

2013
How Correlations Influence Lasso Prediction.
IEEE Trans. Inf. Theory, 2013


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