166 citations · 424 across the 44 of their papers we have counts for
9 papers · 1 filter
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…
Tuning-free ridge estimators for high-dimensional generalized linear models
Shih-Ting Huang, Fang Xie, Johannes Lederer
Ridge estimators regularize the squared Euclidean lengths of parameters. Such estimators are mathematically and computationally attractive but involve tuning parameters that can be…