Johannes Lederer

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All published works

Action	Title	Year	Authors
+	Simultaneous Estimation of Stable Parameters for Multiple Autoregressive Processes From Datasets of Nonuniform Sizes	2025	Johannes Lederer Rainer von Sachs
+ PDF Chat	VC‐PCR: A prediction method based on variable selection and clustering	2024	Rebecca Marion Johannes Lederer Bernadette Goevarts Rainer von Sachs
+	Layer sparsity in neural networks	2024	Mohamed Hebiri Johannes Lederer Mahsa Taheri
+ PDF Chat	Benchmarking the Fairness of Image Upsampling Methods	2024	Mike Laszkiewicz Imant Daunhawer Julia E. Vogt Asja Fischer Johannes Lederer
+ PDF Chat	How many samples are needed to train a deep neural network?	2024	Pegah Golestaneh Mahsa Taheri Johannes Lederer
+ PDF Chat	AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2	2024	Simon Damm Mike Laszkiewicz Johannes Lederer Asja Fischer
+	Benchmarking the Fairness of Image Upsampling Methods	2024	Mike Laszkiewicz Imant Daunhawer Julia E. Vogt Asja Fischer Johannes Lederer
+	Reducing Computational and Statistical Complexity in Machine Learning Through Cardinality Sparsity	2023	Ali Mohades Johannes Lederer
+	The DeepCAR Method: Forecasting Time-Series Data That Have Change Points	2023	Ayla Jungbluth Johannes Lederer
+ PDF Chat	Targeted deep learning: Framework, methods, and applications	2023	Shih‐Ting Huang Johannes Lederer
+	Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming	2023	Somnath Chakraborty Johannes Lederer Rainer von Sachs
+	Extremes in High Dimensions: Methods and Scalable Algorithms	2023	Johannes Lederer Marco Oesting
+	Single-Model Attribution of Generative Models Through Final-Layer Inversion	2023	Mike Laszkiewicz Jonas Ricker Johannes Lederer Asja Fischer
+	Set-Membership Inference Attacks using Data Watermarking	2023	Mike Laszkiewicz Denis Lukovnikov Johannes Lederer Asja Fischer
+	Affine Invariance in Continuous-Domain Convolutional Neural Networks	2023	Ali Mohaddes Johannes Lederer
+ PDF Chat	Balancing Statistical and Computational Precision: A General Theory and Applications to Sparse Regression	2022	Mahsa Taheri Néhémy Lim Johannes Lederer
+ PDF Chat	DeepMoM: Robust Deep Learning With Median-of-Means	2022	Shih‐Ting Huang Johannes Lederer
+ PDF Chat	Topology Adaptive Graph Estimation in High Dimensions	2022	Johannes Lederer Christian L. Müller
+	Depth normalization of small RNA sequencing: using data and biology to select a suitable method	2022	Yannick Düren Johannes Lederer Li‐Xuan Qin
+	Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method	2022	Yannick Düren Johannes Lederer Li‐Xuan Qin
+	VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering	2022	Rebecca Marion Johannes Lederer Bernadette Govaerts Rainer von Sachs
+	Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks	2022	Mahsa Taheri Fang Xie Johannes Lederer
+	Marginal Tail-Adaptive Normalizing Flows	2022	Mike Laszkiewicz Johannes Lederer Asja Fischer
+	Statistical guarantees for sparse deep learning	2022	Johannes Lederer
+	Theory II: Estimation and Support Recovery	2021	Johannes Lederer
+	Theory I: Prediction	2021	Johannes Lederer
+	Tuning-Parameter Calibration	2021	Johannes Lederer
+	Linear Regression	2021	Johannes Lederer
+	Inference	2021	Johannes Lederer
+	Graphical Models	2021	Johannes Lederer
+	Introduction	2021	Johannes Lederer
+ PDF Chat	Is there a role for statistics in artificial intelligence?	2021	Sarah Friedrich Gerd Antes Sigrid Behr Harald Binder Werner Brannath Florian Dumpert Katja Ickstadt Hans A. Kestler Johannes Lederer Heinz Leitgöb
+	Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks.	2021	Leni Ven Johannes Lederer
+ PDF Chat	Statistical guarantees for regularized neural networks	2021	Mahsa Taheri Fang Xie Johannes Lederer
+	Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery	2021	Mike Laszkiewicz Johannes Lederer Asja Fischer
+ PDF Chat	Integrating additional knowledge into the estimation of graphical models	2021	Yunqi Bu Johannes Lederer
+ PDF Chat	Tuning-free ridge estimators for high-dimensional generalized linear models	2021	Shih‐Ting Huang Fang Xie Johannes Lederer
+ PDF Chat	Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data	2021	Fang Xie Johannes Lederer
+	Activation Functions in Artificial Neural Networks: A Systematic Overview	2021	Johannes Lederer
+	Targeted Deep Learning: Framework, Methods, and Applications	2021	Shih‐Ting Huang Johannes Lederer
+	Copula-Based Normalizing Flows	2021	Mike Laszkiewicz Johannes Lederer Asja Fischer
+	Estimating the Lasso's Effective Noise	2021	Johannes Lederer Michael Vogt
+ PDF Chat	Tuning parameter calibration for personalized prediction in medicine	2021	Shih‐Ting Huang Yannick Düren Kristoffer H. Hellton Johannes Lederer
+	Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks	2021	Leni Ven Johannes Lederer
+	DeepMoM: Robust Deep Learning With Median-of-Means	2021	Shih‐Ting Huang Johannes Lederer
+	Is there a role for statistics in artificial intelligence	2020	Sarah Friedrich Gerd Antes Sigrid Behr Harald Binder Werner Brannath Florian Dumpert Katja Ickstadt Hans A. Kestler Johannes Lederer Heinz Leitgöb
+	A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics	2020	Sophie-Charlotte Klose Johannes Lederer
+	Statistical Guarantees for Regularized Neural Networks	2020	Mahsa Taheri Fang Xie Johannes Lederer
+	Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery	2020	Mike Laszkiewicz Asja Fischer Johannes Lederer
+	Estimating the Lasso's Effective Noise	2020	Johannes Lederer Michael Vogt
+	Tuning-free ridge estimators for high-dimensional generalized linear models	2020	Shih‐Ting Huang Fang Xie Johannes Lederer
+	Risk Bounds for Robust Deep Learning	2020	Johannes Lederer
+	Optimization Landscapes of Wide Deep Neural Networks Are Benign	2020	Johannes Lederer
+	A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics	2020	Sophie-Charlotte Klose Johannes Lederer
+	Estimating the Lasso's Effective Noise	2020	Johannes Lederer Michael Vogt
+	Is there a role for statistics in artificial intelligence?	2020	Sarah Friedrich Gerd Antes Sigrid Behr Harald Binder Werner Brannath Florian Dumpert Katja Ickstadt Hans A. Kestler Johannes Lederer Heinz Leitgöb
+	Layer Sparsity in Neural Networks	2020	Mohamed Hebiri Johannes Lederer
+	Statistical Guarantees for Regularized Neural Networks	2020	Mahsa Taheri Fang Xie Johannes Lederer
+	Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery	2020	Mike Laszkiewicz Asja Fischer Johannes Lederer
+ PDF Chat	Inference for high-dimensional instrumental variables regression	2019	David Gold Johannes Lederer Jing Tao
+	False Discovery Rates in Biological Networks	2019	Lu Yu Tobias Kaufmann Johannes Lederer
+	Aggregated False Discovery Rate Control	2019	Fang Xie Johannes Lederer
+ PDF Chat	Oracle inequalities for high-dimensional prediction	2019	Johannes Lederer Yu Lu Irina Gaynanova
+	Tuning parameter calibration for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e1278" altimg="si210.gif"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-regularized logistic regression	2019	Wei Li Johannes Lederer
+	Tuning parameter calibration for prediction in personalized medicine	2019	Shih‐Ting Huang Yannick Düren Kristoffer H. Hellton Johannes Lederer
+	False Discovery Rates in Biological Networks	2019	Lu Yu Tobias Kaufmann Johannes Lederer
+ PDF Chat	Prediction error bounds for linear regression with the TREX	2018	Jacob Bien Irina Gaynanova Johannes Lederer Christian L. Müller
+	Prediction Error Bounds for Linear Regression With the TREX	2018	Jacob Bien Irina Gaynanova Johannes Lederer Christian L. Müller
+ PDF Chat	Maximum regularized likelihood estimators: A general prediction theory and applications	2018	Rui Zhuang Johannes Lederer
+	Prediction Error Bounds for Linear Regression With the TREX	2018	Jacob Bien Irina Gaynanova Johannes Lederer Christian Müller
+	Inference for high-dimensional nested regression	2017	David Gold Johannes Lederer Jing Tao
+ PDF Chat	Optimal two-step prediction in regression	2017	Didier Chételat Johannes Lederer Joseph Salmon
+ PDF Chat	Non-Convex Global Minimization and False Discovery Rate Control for the TREX	2017	Jacob Bien Irina Gaynanova Johannes Lederer Christian L. Müller
+	Integrating Additional Knowledge Into Estimation of Graphical Models	2017	Yunqi Bu Johannes Lederer
+	Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications	2017	Rui Zhuang Johannes Lederer
+	Inference for high-dimensional instrumental variables regression	2017	David Gold Johannes Lederer Jing Tao
+	Optimal two-step prediction in regression	2017	Didier Chételat Johannes Lederer Joseph Salmon
+	Tuning Parameter Calibration in High-dimensional Logistic Regression With Theoretical Guarantees	2016	Wei Li Johannes Lederer
+	On the prediction performance of the Lasso	2016	Arnak S. Dalalyan Mohamed Hebiri Johannes Lederer
+	Efficient Feature Selection With Large and High-dimensional Data	2016	Néhémy Lim Johannes Lederer
+	Balancing Statistical and Computational Precision and Applications to Penalized Linear Regression with Group Sparsity	2016	Mahsa Taheri Néhémy Lim Johannes Lederer
+	Oracle Inequalities for High-dimensional Prediction	2016	Johannes Lederer Yu Lu Irina Gaynanova
+	Graphical Models for Discrete and Continuous Data	2016	Rui Zhuang Noah Simon Johannes Lederer
+	Tuning parameter calibration for $\ell_1$-regularized logistic regression	2016	Wei Li Johannes Lederer
+	A practical scheme and fast algorithm to tune the lasso with optimality guarantees	2016	Michaël Chichignoud Johannes Lederer Martin J. Wainwright
+	Oracle Inequalities for High-dimensional Prediction	2016	Johannes Lederer Yu Lu Irina Gaynanova
+ PDF Chat	Compute Less to Get More: Using ORC to Improve Sparse Filtering	2015	Johannes Lederer Sergio Guadarrama
+ PDF Chat	Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX	2015	Johannes Lederer Christian L. Müller
+	Don't fall for tuning parameters: tuning-free variable selection in high dimensions with the TREX	2015	Johannes Lederer Christian L. Müller
+	Tuning Lasso for sup-norm optimality	2014	Michaël Chichignoud Johannes Lederer Martin J. Wainwright
+	New concentration inequalities for suprema of empirical processes	2014	Johannes Lederer Sara van de Geer
+	A robust, adaptive M-estimator for pointwise estimation in heteroscedastic regression	2014	Michaël Chichignoud Johannes Lederer
+	On the Prediction Performance of the Lasso	2014	Arnak S. Dalalyan Mohamed Hebiri Johannes Lederer
+	A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality Guarantees	2014	Michaël Chichignoud Johannes Lederer Martin J. Wainwright
+	Topology Adaptive Graph Estimation in High Dimensions	2014	Johannes Lederer Christa E. Müller
+	Compute Less to Get More: Using ORC to Improve Sparse Filtering	2014	Johannes Lederer Sergio Guadarrama
+	Optimal Two-Step Prediction in Regression	2014	Didier Chételat Johannes Lederer Joseph Salmon
+	Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX	2014	Johannes Lederer Christian Müller
+ PDF Chat	The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms	2013	Florentina Bunea Johannes Lederer Yiyuan She
+	Trust, but verify: benefits and pitfalls of least-squares refitting in high dimensions	2013	Johannes Lederer
+ PDF Chat	The Lasso, correlated design, and improved oracle inequalities	2013	Sara van de Geer Johannes Lederer
+	The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms	2013	Florentina Bunea Johannes Lederer Yiyuan She
+ PDF Chat	How Correlations Influence Lasso Prediction	2012	Mohamed Hebiri Johannes Lederer
+ PDF Chat	The Bernstein–Orlicz norm and deviation inequalities	2012	Sara van de Geer Johannes Lederer
+ PDF Chat	How Correlations Influence Lasso Prediction	2012	Mohamed Hebiri Johannes Lederer
+	Nonasymptotic bounds for empirical processes and regression	2012	Johannes Lederer
+	How Correlations Influence Lasso Prediction	2012	Mohamed Hebiri Johannes Lederer
+	The Bernstein-Orlicz norm and deviation inequalities	2011	Sara van de Geer Johannes Lederer
+	The Lasso, correlated design, and improved oracle inequalities	2011	Sara van de Geer Johannes Lederer
+	The Bernstein-Orlicz norm and deviation inequalities	2011	Sara van de Geer Johannes Lederer
+	Bounds for Rademacher Processes via Chaining	2010	Johannes Lederer

Common Coauthors

Coauthor	Papers Together
Mike Laszkiewicz	10
Asja Fischer	10
Mohamed Hebiri	7
Shih‐Ting Huang	7
Irina Gaynanova	7
Fang Xie	6
Mahsa Taheri	6
Sara van de Geer	6
Christian L. Müller	6
Michaël Chichignoud	4
Yannick Düren	4
Jacob Bien	4
Rainer von Sachs	4
Sarah Friedrich	3
Adalbert Wilhelm	3
Yu Lu	3
Joseph Salmon	3
Heinz Leitgöb	3
Harald Binder	3
Werner Brannath	3
Sigrid Behr	3
Martin J. Wainwright	3
Ansgar Steland	3
Rui Zhuang	3
Tim Friede	3
Katja Ickstadt	3
Néhémy Lim	3
Markus Pauly	3
Florian Dumpert	3
Didier Chételat	3
Hans A. Kestler	3
David Gold	3
Jing Tao	3
Leni Ven	2
Sergio Guadarrama	2
Li‐Xuan Qin	2
Michael Vogt	2
Sophie-Charlotte Klose	2
Florentina Bunea	2
Imant Daunhawer	2
Rebecca Marion	2
Mahsa Taheri	2
Christian Müller	2
Arnak S. Dalalyan	2
Yiyuan She	2
Julia E. Vogt	2
Yunqi Bu	2
Kristoffer H. Hellton	2
Tobias Kaufmann	2
Fang Xie	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Regression Shrinkage and Selection Via the Lasso	1996	Robert Tibshirani	41
+ PDF Chat	How Correlations Influence Lasso Prediction	2012	Mohamed Hebiri Johannes Lederer	29
+	Statistics for High-Dimensional Data: Methods, Theory and Applications	2011	Peter Bhlmann Sara van de Geer	29
+ PDF Chat	The Lasso, correlated design, and improved oracle inequalities	2013	Sara van de Geer Johannes Lederer	24
+ PDF Chat	Scaled sparse linear regression	2012	Tao Sun C.-H. Zhang	23
+ PDF Chat	On the conditions used to prove oracle results for the Lasso	2009	Sara A. van de Geer Peter Bühlmann	22
+	On the prediction performance of the Lasso	2016	Arnak S. Dalalyan Mohamed Hebiri Johannes Lederer	21
+ PDF Chat	Square-root lasso: pivotal recovery of sparse signals via conic programming	2011	Alexandre Belloni Victor Chernozhukov Lei Wang	21
+	Statistical Learning with Sparsity	2015	Trevor Hastie Robert Tibshirani Martin J. Wainwright	21
+ PDF Chat	Oracle inequalities for high-dimensional prediction	2019	Johannes Lederer Yu Lu Irina Gaynanova	19
+	Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties	2001	Jianqing Fan Runze Li	19
+ PDF Chat	Model Selection and Estimation in Regression with Grouped Variables	2005	Ming Yuan Yi Lin	18
+ PDF Chat	Simultaneous analysis of Lasso and Dantzig selector	2009	Peter J. Bickel Ya’acov Ritov Alexandre B. Tsybakov	18
+	A practical scheme and fast algorithm to tune the lasso with optimality guarantees	2016	Michaël Chichignoud Johannes Lederer Martin J. Wainwright	17
+ PDF Chat	The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms	2013	Florentina Bunea Johannes Lederer Yiyuan She	16
+ PDF Chat	Nearly unbiased variable selection under minimax concave penalty	2010	Cun‐Hui Zhang	15
+	Sparse inverse covariance estimation with the graphical lasso	2007	Jerome H. Friedman Trevor Hastie R. Tibshirani	14
+ PDF Chat	Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors	1997	Oleg Lepski Enno Mammen Vladimir Spokoiny	14
+ PDF Chat	Non-Convex Global Minimization and False Discovery Rate Control for the TREX	2017	Jacob Bien Irina Gaynanova Johannes Lederer Christian L. Müller	14
+	Don't fall for tuning parameters: tuning-free variable selection in high dimensions with the TREX	2015	Johannes Lederer Christian L. Müller	14
+ PDF Chat	Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX	2015	Johannes Lederer Christian L. Müller	14
+	On a Problem of Adaptive Estimation in Gaussian White Noise	1991	O. V. Lepskii	14
+	Optimal two-step prediction in regression	2017	Didier Chételat Johannes Lederer Joseph Salmon	14
+ PDF Chat	Sparse and Compositionally Robust Inference of Microbial Ecological Networks	2015	Zachary Kurtz Christian L. Müller Emily R. Miraldi Dan R. Littman Martin J. Blaser Richard Bonneau	13
+	Cross-Validatory Choice and Assessment of Statistical Predictions	1974	M. Stone	13
+	On Model Selection Consistency of Lasso	2006	Peng Zhao Bin Yu	13
+	Model selection and estimation in the Gaussian graphical model	2007	Ming Yuan Yi Lin	13
+ PDF Chat	Prediction error bounds for linear regression with the TREX	2018	Jacob Bien Irina Gaynanova Johannes Lederer Christian L. Müller	12
+	A Sparse-Group Lasso	2012	Noah Simon Jerome H. Friedman Trevor Hastie Robert Tibshirani	12
+	Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)	2009	Martin J. Wainwright	12
+	Least squares after model selection in high-dimensional sparse models	2013	Alexandre Belloni Victor Chernozhukov	11
+ PDF Chat	A survey of cross-validation procedures for model selection	2010	Sylvain Arlot Alain Célisse	11
+	Regularization Paths for Generalized Linear Models via Coordinate Descent.	2010	Jerome H. Friedman Trevor Hastie Rob Tibshirani	11
+	Tuning parameter calibration for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e1278" altimg="si210.gif"><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math>-regularized logistic regression	2019	Wei Li Johannes Lederer	11
+	Persistence in high-dimensional linear predictor selection and the virtue of overparametrization	2004	Eitan Greenshtein Ya’acov Ritov	11
+ PDF Chat	Structured Regularizers for High-Dimensional Problems: Statistical and Computational Issues	2014	Martin J. Wainwright	11
+	High-dimensional graphs and variable selection with the Lasso	2006	Nicolai Meinshausen Peter Bühlmann	10
+	Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter	1979	Gene H. Golub Michael T. Heath Grace Wahba	10
+	On asymptotically optimal confidence regions and tests for high-dimensional models	2014	Sara van de Geer Peter Bühlmann Ya’acov Ritov Ruben Dezeure	10
+ PDF Chat	A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection	2015	Jeremy Sabourin William Valdar Andrew B. Nobel	10
+ PDF Chat	Least angle regression	2004	Bradley Efron Trevor Hastie Iain M. Johnstone Robert Tibshirani	10
+	The Statistical Analysis of Compositional Data	1982	J. Aitchison	10
+ PDF Chat	Sparsity oracle inequalities for the Lasso	2007	Florentina Bunea Alexandre B. Tsybakov Marten Wegkamp	10
+	Comments on: ℓ 1-penalization for mixture regression models	2010	Anestis Antoniadis	10
+	Spatial Interaction and the Statistical Analysis of Lattice Systems	1974	Julian Besag	10
+ PDF Chat	ℓ1-penalization for mixture regression models	2010	Nicolas Städler Peter Bühlmann Sara van de Geer	10
+ PDF Chat	The Bernstein–Orlicz norm and deviation inequalities	2012	Sara van de Geer Johannes Lederer	9
+	Ridge Regression: Biased Estimation for Nonorthogonal Problems	1970	Arthur E. Hoerl Robert W. Kennard	9
+	Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion	2011	Vladimir Koltchinskii Karim Lounici Alexandre B. Tsybakov	9
+ PDF Chat	Inference for high-dimensional instrumental variables regression	2019	David Gold Johannes Lederer Jing Tao	8