Adrian Englhardt

According to our database, Adrian Englhardt authored at least 15 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking.

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

CoRR, February, 2026

2025

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, Natesh Sivasubramoniapillai, Necip Fazil Ayan, Qingquan Song, Samira Sriram, Souvik Ghosh, Tao Song, Vignesh Kothapalli, Xiaoling Zhai, Ya Xu, Yu Wang, Yun Dai

CoRR, January, 2025

2022

Efficient SVDD sampling with approximation guarantees for the decision boundary.

Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm

Mach. Learn., 2022

2021

Cost-Quality Trade-Offs in One-Class Active Learning.

Adrian Englhardt

PhD thesis, 2021

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

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

Toward meaningful notions of similarity in NLP embedding models.

Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm

Int. J. Digit. Libr., 2020

Finding the Sweet Spot: Batch Selection for One-Class Active Learning.

Adrian Englhardt, Holger Trittenbach, Dennis Vetter, Klemens Böhm

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Exploring the Unknown - Query Synthesis in One-Class Active Learning.

Adrian Englhardt, Klemens Böhm

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

2019

Energy Time-Series Features for Emerging Applications on the Basis of Human-Readable Machine Descriptions.

Michael Vollmer, Adrian Englhardt, Holger Trittenbach, Pawel Bielski, Shahab Karrari, Klemens Böhm

Proceedings of the Tenth ACM International Conference on Future Energy Systems, 2019

2018

An Overview and a Benchmark of Active Learning for One-Class Classification.

Holger Trittenbach, Adrian Englhardt, Klemens Böhm

CoRR, 2018

Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data.

Ábel Elekes, Adrian Englhardt, Martin Schäler, Klemens Böhm

Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018

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

How to Quantify the Impact of Lossy Transformations on Event Detection.

Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm

Big Data Res., 2017

2015

How to quantify the impact of lossy transformations on change detection.

Pavel Efros, Erik Buchmann, Adrian Englhardt, Klemens Böhm

Proceedings of the 27th International Conference on Scientific and Statistical Database Management, 2015