How to quantify the impact of lossy transformations on change detection. - Pavel Efros; Erik Buchmann; Adrian Englhardt; Klemens Böhm (SSDBM, 2015)

How to Quantify the Impact of Lossy Transformations on Event Detection. - Pavel Efros; Erik Buchmann; Adrian Englhardt; Klemens Böhm (Big Data Res., 2017)

An evaluation of combinations of lossy compression and change-detection approaches for time-series data. - Gregor Hollmig; Matthias Horne; Simon Leimkühler; Frederik Schöll; Carsten Strunk; Adrian Englhardt; Pavel Efros; Erik Buchmann; Klemens Böhm (Inf. Syst., 2017)

Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data. - Ábel Elekes; Adrian Englhardt; Martin Schäler; Klemens Böhm (CoNLL, 2018)

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

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)

Exploring the Unknown - Query Synthesis in One-Class Active Learning. - Adrian Englhardt; Klemens Böhm (SDM, 2020)

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

Toward meaningful notions of similarity in NLP embedding models. - Ábel Elekes; Adrian Englhardt; Martin Schäler; Klemens Böhm (Int. J. Digit. Libr., 2020)

Improving semantic change analysis by combining word embeddings and word frequencies. - Adrian Englhardt; Jens Willkomm; Martin Schäler; Klemens Böhm (Int. J. Digit. Libr., 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)

Cost-Quality Trade-Offs in One-Class Active Learning. - Adrian Englhardt (2021)

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

360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation. - Hamed Firooz; Maziar Sanjabi; Adrian Englhardt; Aman Gupta; Ben Levine; Dre Olgiati; Gungor Polatkan; Iuliia Melnychuk; Karthik Ramgopal; Kirill Talanine; Kutta Srinivasan; Luke Simon; et al. (CoRR, 2025)