Holger Trittenbach

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Klemens BöhmUniv.-Prof. Dr.-Ing., IPD - Institute of Program Structures and Data OrganizationVerified email at kit.edu
Adrian EnglhardtAI Engineer @ LinkedIn | Previously: Researcher, Karlsruhe Institute of TechnologyVerified email at kit.edu
Michael VollmerResearcher, Karlsruhe Institute of TechnologyVerified email at kit.edu
Dominik WerleResearcher, Karlsruhe Institute of Technology (KIT)Verified email at kit.edu
Jakob BachPhD student, Karlsruhe Institute of TechnologyVerified email at kit.edu
Ira AssentProfessor of Computer Science, Aarhus University, Denmark; IAS-8, Forschungszentrum JülichVerified email at cs.au.dk
Dennis VetterGoethe University FrankfurtVerified email at em.uni-frankfurt.de
Martin GauchJohannes Kepler University Linz, Institute for Machine LearningVerified email at ml.jku.at
Shahab KarrariControl Expert at Siemens EnergyVerified email at siemens-energy.com
Daniel KottkeResearcher, Kassel UniversityVerified email at uni-kassel.de
Bernhard SickProfessor of Intelligent Embedded Systems, University of KasselVerified email at uni-kassel.de
Anne KoziolekProfessor, Karlsruhe Institute of Technology, GermanyVerified email at kit.edu
Reinhard HeilResearcher, Institute for Technology Assessment and Systems Analysis (ITAS/KIT)Verified email at kit.edu
Michael PoznicKarlsruhe Institute of Technology, Institute for Technology Assessment and Systems Analysis (ITASVerified email at kit.edu
Moritz RenftleM.Sc. Computer ScienceVerified email at renftle.net

Holger Trittenbach

neurocat GmbH

Verified email at neurocat.ai - Homepage

Active Learning Anomaly Detection One-Class Classification Explainable AI Robust AI


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An overview and a benchmark of active learning for outlier detection with one-class classifiers H Trittenbach, A Englhardt, K Böhm Expert Systems with Applications 168, 114372, 2021	36	2021
HIPE: An energy-status-data set from industrial production S Bischof, H Trittenbach, M Vollmer, D Werle, T Blank, K Böhm Proceedings of the Ninth International Conference on Future Energy Systems …, 2018	31	2018
Dimension-based subspace search for outlier detection H Trittenbach, K Böhm International Journal of Data Science and Analytics 7 (2), 87-101, 2019	19	2019
One-Class Active Learning for Outlier Detection with Multiple Subspaces H Trittenbach, K Böhm Proceedings of the 28th ACM International Conference on Information and …, 2019	10	2019
Validating One-Class Active Learning with User Studies–a Prototype and Open Challenges H Trittenbach, A Englhardt, K Böhm ECML PKDD Workshop, 2019	10	2019
Active Learning of SVDD Hyperparameter Values H Trittenbach, K Böhm, I Assent 2020 IEEE 7th International Conference on Data Science and Advanced …, 2020	7	2020
Finding the Sweet Spot: Batch Selection for One-Class Active Learning A Englhardt, H Trittenbach, D Vetter, K Böhm Proceedings of the 2020 SIAM International Conference on Data Mining, 118-126, 2020	6	2020
Towards Simulation-Data Science–A Case Study on Material Failures H Trittenbach, M Gauch, K Böhm, K Schulz 2018 IEEE 5th International Conference on Data Science and Advanced …, 2018	5	2018
On the Tradeoff between Energy Data Aggregation and Clustering Quality H Trittenbach, J Bach, K Böhm Proceedings of the Ninth International Conference on Future Energy Systems …, 2018	5	2018
Efficient SVDD sampling with approximation guarantees for the decision boundary A Englhardt, H Trittenbach, D Kottke, B Sick, K Böhm Machine Learning, 1-27, 2022	3	2022
Data-driven crack assessment based on surface measurements K Schulz, S Kreis, H Trittenbach, K Böhm Engineering Fracture Mechanics 218, 106552, 2019	3	2019
Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions M Vollmer, H Trittenbach, S Karrari, A Englhardt, P Bielski, K Böhm Proceedings of the Tenth ACM International Conference on Future Energy …, 2019	3	2019
User-Centric Active Learning for Outlier Detection. H Trittenbach Karlsruhe Institute of Technology, Germany, 2020	2	2020
The Effect of Temporal Aggregation on Battery Sizing for Peak Shaving D Werle, S Bischof, H Trittenbach, D Warzel, A Koziolek, K Böhm Proceedings of the Tenth ACM International Conference on Future Energy …, 2019	2	2019
Understanding the effects of temporal energy-data aggregation on clustering quality H Trittenbach, J Bach, K Böhm it-Information Technology 61 (2-3), 111-123, 2019	2	2019
Explaining Any ML Model?--On Goals and Capabilities of XAI M Renftle, H Trittenbach, M Poznic, R Heil arXiv preprint arXiv:2206.13888, 2022	1	2022
Selecting Models based on the Risk of Damage Caused by Adversarial Attacks J Klemenc, H Trittenbach arXiv preprint arXiv:2301.12151, 2023		2023
An Empirical Evaluation of Constrained Feature Selection J Bach, K Zoller, H Trittenbach, K Schulz, K Böhm SN Computer Science 3 (6), 445, 2022		2022

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