Towards Simulation-Data Science - A Case Study on Material Failures. - Holger Trittenbach; Martin Gauch; Klemens Böhm; Katrin Schulz (DSAA, 2018)

HIPE: An Energy-Status-Data Set from Industrial Production. - Simon Bischof; Holger Trittenbach; Michael Vollmer 001; Dominik Werle; Thomas Blank; Klemens Böhm (e-Energy, 2018)

On the Tradeoff between Energy Data Aggregation and Clustering Quality. - Holger Trittenbach; Jakob Bach; Klemens Böhm (e-Energy, 2018)

An Overview and a Benchmark of Active Learning for One-Class Classification. - Holger Trittenbach; Adrian Englhardt; Klemens Böhm (CoRR, 2018)

High-resolution Industrial Production Energy (HIPE). - Simon Bischof; Holger Trittenbach; Dominik Werle; Thomas Blank; Klemens Böhm (Zenodo, 2018)

One-Class Active Learning for Outlier Detection with Multiple Subspaces. - Holger Trittenbach; Klemens Böhm (CIKM, 2019)

Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions. - Michael Vollmer 001; Adrian Englhardt; Holger Trittenbach; Pawel Bielski; Shahab Karrari; Klemens Böhm (e-Energy, 2019)

The Effect of Temporal Aggregation on Battery Sizing for Peak Shaving. - Dominik Werle; Daniel Warzel; Simon Bischof; Anne Koziolek; Holger Trittenbach; Klemens Böhm (e-Energy, 2019)

Dimension-based subspace search for outlier detection. - Holger Trittenbach; Klemens Böhm (Int. J. Data Sci. Anal., 2019)

Understanding the effects of temporal energy-data aggregation on clustering quality. - Holger Trittenbach; Jakob Bach; Klemens Böhm (it Inf. Technol., 2019)

High-resolution Industrial Production Energy (HIPE). - Simon Bischof; Holger Trittenbach; Dominik Werle; Thomas Blank; Klemens Böhm (Zenodo, 2019)

Active Learning of SVDD Hyperparameter Values. - Holger Trittenbach; Klemens Böhm; Ira Assent (DSAA, 2020)

Finding the Sweet Spot: Batch Selection for One-Class Active Learning. - Adrian Englhardt; Holger Trittenbach; Dennis Vetter; Klemens Böhm (SDM, 2020)

User-Centric Active Learning for Outlier Detection. - Holger Trittenbach (2020)

An overview and a benchmark of active learning for outlier detection with one-class classifiers. - Holger Trittenbach; Adrian Englhardt; Klemens Böhm (Expert Syst. Appl., 2021)

Explaining Any ML Model? - On Goals and Capabilities of XAI. - Moritz Renftle; Holger Trittenbach; Michael Poznic; Reinhard Heil (CoRR, 2022)

Efficient SVDD sampling with approximation guarantees for the decision boundary. - Adrian Englhardt; Holger Trittenbach; Daniel Kottke; Bernhard Sick; Klemens Böhm (Mach. Learn., 2022)

An Empirical Evaluation of Constrained Feature Selection. - Jakob Bach; Kolja Zoller; Holger Trittenbach; Katrin Schulz; Klemens Böhm (SN Comput. Sci., 2022)

Selecting Models based on the Risk of Damage Caused by Adversarial Attacks. - Jona Klemenc; Holger Trittenbach (CoRR, 2023)

A-BDD: Leveraging Data Augmentations for Safe Autonomous Driving in Adverse Weather and Lighting. - Felix Assion; Florens Greßner; Nitin Augustine; Jona Klemenc; Ahmed Hammam; Alexandre Krattinger; Holger Trittenbach; Sascha Riemer (CoRR, 2024)