On Privacy in Spatio-Temporal Data: User Identification Using Microblog Data. - Erik Seglem; Andreas Züfle; Jan Stutzki; Felix Borutta; Evgheniy Faerman; Matthias Schubert (SSTD, 2017)

LASAGNE: Locality and Structure Aware Graph Node Embedding. - Evgheniy Faerman; Felix Borutta; Kimon Fountoulakis; Michael W. Mahoney (WI, 2018)

Semi-Supervised Learning on Graphs Based on Local Label Distributions. - Evgheniy Faerman; Felix Borutta; Julian Busch; Matthias Schubert (CoRR, 2018)

Spatial Interpolation with Message Passing Framework. - Evgeniy Faerman; Manuell Rogalla; Niklas Strauß; Adrian Krüger; Benedict Blümel; Max Berrendorf; Michael Fromm 001; Matthias Schubert (ICDM Workshops, 2019)

XD-STOD: Cross-Domain Superresolution for Tiny Object Detection. - Michael Fromm 001; Max Berrendorf; Evgeniy Faerman; Yiyi Chen 002; Balthasar Schüss; Matthias Schubert (ICDM Workshops, 2019)

Structural Graph Representations based on Multiscale Local Network Topologies. - Felix Borutta; Julian Busch; Evgeniy Faerman; Adina Klink; Matthias Schubert (WI, 2019)

TACAM: Topic And Context Aware Argument Mining. - Michael Fromm 001; Evgeniy Faerman; Thomas Seidl 001 (WI, 2019)

Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. - Valentyn Melnychuk; Evgeniy Faerman; Ilja Manakov; Thomas Seidl 001 (CIKM (Workshops), 2020)

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. - Max Berrendorf; Evgeniy Faerman; Valentyn Melnychuk; Volker Tresp; Thomas Seidl 001 (ECIR (2), 2020)

Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods. - Max Berrendorf; Evgeniy Faerman; Laurent Vermue; Volker Tresp (WI/IAT, 2020)

Ada-LLD: Adaptive Node Similarity Using Multi-Scale Local Label Distributions. - Evgeniy Faerman; Felix Borutta; Julian Busch; Matthias Schubert (WI/IAT, 2020)

Unsupervised Anomaly Detection for X-Ray Images. - Diana Davletshina; Valentyn Melnychuk; Viet Tran; Hitansh Singla; Max Berrendorf; Evgeniy Faerman; Michael Fromm 001; Matthias Schubert (CoRR, 2020)

Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods with Adjusted Mean Rank. - Max Berrendorf; Evgeniy Faerman; Laurent Vermue; Volker Tresp (CoRR, 2020)

Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering. - Julian Busch; Evgeniy Faerman; Matthias Schubert; Thomas Seidl 001 (CoRR, 2020)

Argument Mining Driven Analysis of Peer-Reviews Dataset. - Michael Fromm 001; Max Berrendorf; Evgheniy Faerman; Thomas Seidl 001 (Zenodo, 2020)

Argument Mining Driven Analysis of Peer-Reviews. - Michael Fromm 001; Evgeniy Faerman; Max Berrendorf; Siddharth Bhargava; Ruoxia Qi; Yao Zhang; Lukas Dennert; Sophia Selle; Yang Mao; Thomas Seidl 001 (AAAI, 2021)

Active Learning for Entity Alignment. - Max Berrendorf; Evgeniy Faerman; Volker Tresp (ECIR (1), 2021)

A Critical Assessment of State-of-the-Art in Entity Alignment. - Max Berrendorf; Ludwig Wacker; Evgeniy Faerman (ECIR (2), 2021)

Diversity Aware Relevance Learning for Argument Search. - Michael Fromm 001; Max Berrendorf; Sandra Obermeier; Thomas Seidl 001; Evgeniy Faerman (ECIR (2), 2021)

Prediction of soft proton intensities in the near-Earth space using machine learning. - Elena A. Kronberg; Tanveer Hannan; Jens Huthmacher; Marcus Münzer; Florian Peste; Ziyang Zhou; Max Berrendorf; Evgeniy Faerman; Fabio Gastaldello; Simona Ghizzardi; Philippe Escoubet; Stein Haaland; et al. (CoRR, 2021)

Active Learning for Argument Strength Estimation. - Nataliia Kees; Michael Fromm 001; Evgeniy Faerman; Thomas Seidl 001 (CoRR, 2021)

Representation learning on relational data. - Evgeniy Faerman (2021)

Towards a Holistic View on Argument Quality Prediction. - Michael Fromm 001; Max Berrendorf; Johanna Reiml; Isabelle Mayerhofer; Siddharth Bhargava; Evgeniy Faerman; Thomas Seidl 001 (CoRR, 2022)

Cross-Domain Argument Quality Estimation. - Michael Fromm 001; Max Berrendorf; Evgeniy Faerman; Thomas Seidl 001 (ACL (Findings), 2023)