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pro vyhledávání: '"Hoppe, Frederik"'
One of the most promising solutions for uncertainty quantification in high-dimensional statistics is the debiased LASSO that relies on unconstrained $\ell_1$-minimization. The initial works focused on real Gaussian designs as a toy model for this pro
Externí odkaz:
http://arxiv.org/abs/2407.18964
Over the last few years, debiased estimators have been proposed in order to establish rigorous confidence intervals for high-dimensional problems in machine learning and data science. The core argument is that the error of these estimators with respe
Externí odkaz:
http://arxiv.org/abs/2407.13575
Uncertainty quantification (UQ) is a crucial but challenging task in many high-dimensional regression or learning problems to increase the confidence of a given predictor. We develop a new data-driven approach for UQ in regression that applies both t
Externí odkaz:
http://arxiv.org/abs/2407.13666
Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the
Externí odkaz:
http://arxiv.org/abs/2309.07982
One of the most prominent methods for uncertainty quantification in high-dimen-sional statistics is the desparsified LASSO that relies on unconstrained $\ell_1$-minimization. The majority of initial works focused on real (sub-)Gaussian designs. Howev
Externí odkaz:
http://arxiv.org/abs/2212.14864
Autor:
Hoppe, Frederik Armin
Protein A wird als Matrix für die Proteinreinigung während der Produktion von monoklonalen Antikörpern verwendet. Um die Wiederverwendbarkeit des teuren Protein A Mediums zu erhöhen, wurden zwei verschiedene Filterkombinationen ausgetestet und be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_____10650::30802a8203c1c3f6f5049e34ece311ae
Autor:
Maurer, Michael, Hoppe, Frederik, Schillinger, Harald, Hutter, Melanie, Kunert, Renate, Eyer, Kurt
Publikováno v:
In New BIOTECHNOLOGY July 2014 31 Supplement:S117-S118