14 citations · 18 across the 9 of their papers we have counts for
10 papers
Double descent for least-squares interpolation on contaminated data: A simulation study
Tino Werner
Overparametrized models can exhibit an excellent generalization performance, although they should be prone to overfitting according to classical statistical theory. The discovery o…
Robust Statistics meets elicitability: When fair model validation breaks down
Tino Werner
A crucial part of data analysis is the validation of the resulting estimators, in particular, if several competing estimators need to be compared. Whether an estimator can be objec…
Global quantitative robustness of regression feed-forward neural networks
Tino Werner
Neural networks are an indispensable model class for many complex learning tasks. Despite the popularity and importance of neural networks and many different established techniques…
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
Julian Wörmann, Daniel Bogdoll, Christian Brunner +49
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. However, in real life applications…
Loss-guided Stability Selection
Tino Werner
In modern data analysis, sparse model selection becomes inevitable once the number of predictors variables is very high. It is well-known that model selection procedures like the L…
Trimming Stability Selection increases variable selection robustness
Tino Werner
Contamination can severely distort an estimator unless the estimation procedure is suitably robust. This is a well-known issue and has been addressed in Robust Statistics, however,…
Quantitative robustness of instance ranking problems
Tino Werner
Instance ranking problems intend to recover the true ordering of the instances in a data set with a variety of applications in for example scientific, social and financial contexts…
The column measure and Gradient-Free Gradient Boosting
Tino Werner, Peter Ruckdeschel
Sparse model selection by structural risk minimization leads to a set of a few predictors, ideally a subset of the true predictors. This selection clearly depends on the underlying…
Asymptotic linear expansion of regularized M-estimators
Tino Werner
Parametric high-dimensional regression analysis requires the usage of regularization terms to get interpretable models. The respective estimators can be regarded as regularized M-f…
A review on ranking problems in statistical learning
Tino Werner
Ranking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, docum…