Adrian Englhardt

16 papers A 2B 2Journal 10Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking.
CoRR
Lars Hertel, Gaurav Srivastava, Syed Ali Naqvi, Satyam Kumar, Yue Zhang, Borja Ocejo, Benjamin Zelditch, Adrian Englhardt, Hailing Cheng, Andy Hu, Antonio Alonso, Daming Li, Siddharth Dangi, Chen Zhu, Mingzhou Zhou, Wanning Li, Tao Huang, Fedor Borisyuk, Ganesh Parameswaran, Birjodh Singh Tiwana, Sriram Sankar, Qing Lan, Julie Choi, Souvik Ghosh
2025 J jnl
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation.
CoRR
Hamed Firooz, Maziar Sanjabi, Adrian Englhardt, Aman Gupta, Ben Levine, Dre Olgiati, Gungor Polatkan, Iuliia Melnychuk, Karthik Ramgopal, Kirill Talanine, Kutta Srinivasan, Luke Simon, Natesh Sivasubramoniapillai, Necip Fazil Ayan, Qingquan Song, Samira Sriram, Souvik Ghosh, Tao Song, Vignesh Kothapalli, Xiaoling Zhai, Ya Xu, Yu Wang, Yun Dai
2022 J jnl
Efficient SVDD sampling with approximation guarantees for the decision boundary.
Mach. Learn.
Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm
2021 J jnl
An overview and a benchmark of active learning for outlier detection with one-class classifiers.
Expert Syst. Appl.
Holger Trittenbach, Adrian Englhardt, Klemens Böhm
2021 — —
Cost-Quality Trade-Offs in One-Class Active Learning.
Adrian Englhardt
2020 J jnl
Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary.
CoRR
Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm
2020 A conf
Exploring the Unknown - Query Synthesis in One-Class Active Learning.
SDM
Adrian Englhardt, Klemens Böhm
2020 A conf
Finding the Sweet Spot: Batch Selection for One-Class Active Learning.
SDM
Adrian Englhardt, Holger Trittenbach, Dennis Vetter, Klemens Böhm
2020 J jnl
Improving semantic change analysis by combining word embeddings and word frequencies.
Int. J. Digit. Libr.
Adrian Englhardt, Jens Willkomm, Martin Schäler, Klemens Böhm
2020 J jnl
Toward meaningful notions of similarity in NLP embedding models.
Int. J. Digit. Libr.
Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm
2019 — conf
Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions.
e-Energy
Michael Vollmer, Adrian Englhardt, Holger Trittenbach, Pawel Bielski, Shahab Karrari, Klemens Böhm
2018 J jnl
An Overview and a Benchmark of Active Learning for One-Class Classification.
CoRR
Holger Trittenbach, Adrian Englhardt, Klemens Böhm
2018 B conf
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data.
CoNLL
Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm
2017 J jnl
An evaluation of combinations of lossy compression and change-detection approaches for time-series data.
Inf. Syst.
Gregor Hollmig, Matthias Horne, Simon Leimkühler, Frederik Schöll, Carsten Strunk, Adrian Englhardt, Pavel Efros, Erik Buchmann, Klemens Böhm
2017 J jnl
How to Quantify the Impact of Lossy Transformations on Event Detection.
Big Data Res.
Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm
2015 B conf
How to quantify the impact of lossy transformations on change detection.
SSDBM
Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm