166 citations · 424 across the 44 of their papers we have counts for
50 papers
Market-Informed Networks for Modeling and Forecast Evaluation of Financial Extremes
Ayla Jungbluth, Johannes Lederer, Simon Trimborn
Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not nece…
Set-Membership Inference Attacks using Data Watermarking
Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer +1
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques. In particular, we demonstrate how conditional sampling fr…
Single-Model Attribution of Generative Models Through Final-Layer Inversion
Mike Laszkiewicz, Jonas Ricker, Johannes Lederer +1
Recent breakthroughs in generative modeling have sparked interest in practical single-model attribution. Such methods predict whether a sample was generated by a specific generator…
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming
Somnath Chakraborty, Johannes Lederer, Rainer von Sachs
Motivated by a variety of applications, high-dimensional time series have become an active topic of research. In particular, several methods and finite-sample theories for individu…
Extremes in High Dimensions: Methods and Scalable Algorithms
Johannes Lederer, Marco Oesting
Extreme value theory for univariate and low-dimensional observations has been explored in considerable detail, but the field is still in an early stage regarding high-dimensional s…
The DeepCAR Method: Forecasting Time-Series Data That Have Change Points
Ayla Jungbluth, Johannes Lederer
Many methods for time-series forecasting are known in classical statistics, such as autoregression, moving averages, and exponential smoothing. The DeepAR framework is a novel, rec…
Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning
Ali Mohades, Johannes Lederer
High-dimensional data has become ubiquitous across the sciences but presents computational and statistical challenges. A common approach to addressing these challenges is through s…
Statistical guarantees for sparse deep learning
Johannes Lederer
Neural networks are becoming increasingly popular in applications, but our mathematical understanding of their potential and limitations is still limited. In this paper, we further…
Marginal Tail-Adaptive Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the number of samples from the tail is small, and deep generative models, such as no…
VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering
Rebecca Marion, Johannes Lederer, Bernadette Govaerts +1
Sparse linear prediction methods suffer from decreased prediction accuracy when the predictor variables have cluster structure (e.g. there are highly correlated groups of variables…
Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method
Yannick Düren, Johannes Lederer, Li-Xuan Qin
Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for "normalizing" seq…
Copula-Based Normalizing Flows
Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressi…
Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks
Leni Ven, Johannes Lederer
Deep learning requires several design choices, such as the nodes' activation functions and the widths, types, and arrangements of the layers. One consideration when making these ch…
Targeted Deep Learning: Framework, Methods, and Applications
Shih-Ting Huang, Johannes Lederer
Deep learning systems are typically designed to perform for a wide range of test inputs. For example, deep learning systems in autonomous cars are supposed to deal with traffic sit…
DeepMoM: Robust Deep Learning With Median-of-Means
Shih-Ting Huang, Johannes Lederer
Data used in deep learning is notoriously problematic. For example, data are usually combined from diverse sources, rarely cleaned and vetted thoroughly, and sometimes corrupted on…
Activation Functions in Artificial Neural Networks: A Systematic Overview
Johannes Lederer
Activation functions shape the outputs of artificial neurons and, therefore, are integral parts of neural networks in general and deep learning in particular. Some activation funct…
Optimization Landscapes of Wide Deep Neural Networks Are Benign
Johannes Lederer
We analyze the optimization landscapes of deep learning with wide networks. We highlight the importance of constraints for such networks and show that constraint -- as well as unco…
Risk Bounds for Robust Deep Learning
Johannes Lederer
It has been observed that certain loss functions can render deep-learning pipelines robust against flaws in the data. In this paper, we support these empirical findings with statis…
Is there a role for statistics in artificial intelligence?
Sarah Friedrich, Gerd Antes, Sigrid Behr +11
The research on and application of artificial intelligence (AI) has triggered a comprehensive scientific, economic, social and political discussion. Here we argue that statistics,…
Layer Sparsity in Neural Networks
Mohamed Hebiri, Johannes Lederer
Sparsity has become popular in machine learning, because it can save computational resources, facilitate interpretations, and prevent overfitting. In this paper, we discuss sparsit…
A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics
Sophie-Charlotte Klose, Johannes Lederer
We introduce tools for controlled variable selection to economists. In particular, we apply a recently introduced aggregation scheme for false discovery rate (FDR) control to Germa…
Statistical Guarantees for Regularized Neural Networks
Mahsa Taheri, Fang Xie, Johannes Lederer
Neural networks have become standard tools in the analysis of data, but they lack comprehensive mathematical theories. For example, there are very few statistical guarantees for le…
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
Mike Laszkiewicz, Asja Fischer, Johannes Lederer
Many Machine Learning algorithms are formulated as regularized optimization problems, but their performance hinges on a regularization parameter that needs to be calibrated to each…
Estimating the Lasso's Effective Noise
Johannes Lederer, Michael Vogt
Much of the theory for the lasso in the linear model hinges on the quantity , which we call the lasso's effective…