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pro vyhledávání: '"Werner, Tino"'
Autor:
Werner, Tino
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 objectively validated is not a trivial property. If there exists a loss fun
Externí odkaz:
http://arxiv.org/abs/2405.09943
Autor:
Werner, Tino
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 from literature for stabilization and robustification of the training
Externí odkaz:
http://arxiv.org/abs/2211.10124
Autor:
Wörmann, Julian, Bogdoll, Daniel, Brunner, Christian, Bührle, Etienne, Chen, Han, Chuo, Evaristus Fuh, Cvejoski, Kostadin, van Elst, Ludger, Gottschall, Philip, Griesche, Stefan, Hellert, Christian, Hesels, Christian, Houben, Sebastian, Joseph, Tim, Keil, Niklas, Kelsch, Johann, Keser, Mert, Königshof, Hendrik, Kraft, Erwin, Kreuser, Leonie, Krone, Kevin, Latka, Tobias, Mattern, Denny, Matthes, Stefan, Motzkus, Franz, Munir, Mohsin, Nekolla, Moritz, Paschke, Adrian, von Pilchau, Stefan Pilar, Pintz, Maximilian Alexander, Qiu, Tianming, Qureishi, Faraz, Rizvi, Syed Tahseen Raza, Reichardt, Jörg, von Rueden, Laura, Sagel, Alexander, Sasdelli, Diogo, Scholl, Tobias, Schunk, Gerhard, Schwalbe, Gesina, Shen, Hao, Shoeb, Youssef, Stapelbroek, Hendrik, Stehr, Vera, Srinivas, Gurucharan, Tran, Anh Tuan, Vivekanandan, Abhishek, Wang, Ya, Wasserrab, Florian, Werner, Tino, Wirth, Christian, Zwicklbauer, Stefan
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models. However, in real life applications these models often encounter scenarios that are inadequately represen
Externí odkaz:
http://arxiv.org/abs/2205.04712
Autor:
Werner, Tino
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 Lasso or Boosting tend to overfit on real data. The celebrated Stabilit
Externí odkaz:
http://arxiv.org/abs/2202.04956
Autor:
Werner, Tino
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, the relation of contamination and distorted variable selection has be
Externí odkaz:
http://arxiv.org/abs/2111.11818
Autor:
Werner, Tino
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. Robust statistics studies the behaviour of estimators in the presenc
Externí odkaz:
http://arxiv.org/abs/2103.07198
Autor:
Werner, Tino, Ruckdeschel, Peter
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 loss function $\tilde L$. For linear regression with square loss, the
Externí odkaz:
http://arxiv.org/abs/1909.10960
Autor:
Werner, Tino
Ranking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine, credit risk screening, image ranking or media m
Externí odkaz:
http://arxiv.org/abs/1909.02998
Autor:
Werner, Tino
Parametric high-dimensional regression analysis requires the usage of regularization terms to get interpretable models. The respective estimators can be regarded as regularized M-functionals which are naturally highly nonlinear. We study under which
Externí odkaz:
http://arxiv.org/abs/1909.00579
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