activity
20192025
most citedFeature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

5 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

physics.atom-ph2025★ 1 cited

Extracting RABBITT-like phase information from time-dependent transient absorption spectra

J. Jakob, C. Bauer, M. -J. Ilhan +5

We explore how the spectral phase of atto\-second pulse trains influences the optical cross section in transient absorption (TA) spectroscopy. The interaction of extreme ultraviole…

cs.LG2022★ 1 cited

SAM-kNN Regressor for Online Learning in Water Distribution Networks

Jonathan Jakob, André Artelt, Martina Hasenjäger +1

Water distribution networks are a key component of modern infrastructure for housing and industry. They transport and distribute water via widely branched networks from sources to…

cs.LG2020★ 2 cited

Analysis of Drifting Features

Fabian Hinder, Jonathan Jakob, Barbara Hammer

The notion of concept drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. We are interested in an identification of thos…

cs.LG2019★ 5 cited

Feature Relevance Determination for Ordinal Regression in the Context of Feature Redundancies and Privileged Information

Lukas Pfannschmidt, Jonathan Jakob, Fabian Hinder +3

Advances in machine learning technologies have led to increasingly powerful models in particular in the context of big data. Yet, many application scenarios demand for robustly int…

cs.LG2019★ 1 cited

Feature Relevance Bounds for Ordinal Regression

Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl +2

The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model…