Optimal k-Nearest-Neighbor Query Processing via Multiple Lower Bound Approximations. - Christian Beecks; Max Berrendorf (IEEE BigData, 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)

k-Distance Approximation for Memory-Efficient RkNN Retrieval. - Max Berrendorf; Felix Borutta; Peer Kröger (SISAP, 2019)

Improving Visual Relation Detection using Depth Maps. - Sahand Sharifzadeh; Max Berrendorf; Volker Tresp (CoRR, 2019)

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

Improving Visual Relation Detection using Depth Maps. - Sahand Sharifzadeh; Sina Moayed Baharlou; Max Berrendorf; Rajat Koner; Volker Tresp (ICPR, 2020)

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

Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation. - Sandra Obermeier; Max Berrendorf; Peer Kröger (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)

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)

Improving Inductive Link Prediction Using Hyper-relational Facts. - Mehdi Ali; Max Berrendorf; Mikhail Galkin 001; Veronika Thost; Tengfei Ma 001; Volker Tresp; Jens Lehmann 001 (ISWC, 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)

On the Generalization of Agricultural Drought Classification from Climate Data. - Julia Gottfriedsen; Max Berrendorf; Pierre Gentine; Markus Reichstein; Katja Weigel; Birgit Hassler; Veronika Eyring (CoRR, 2021)

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings. - Mehdi Ali; Max Berrendorf; Charles Tapley Hoyt; Laurent Vermue; Sahand Sharifzadeh; Volker Tresp; Jens Lehmann 001 (J. Mach. Learn. Res., 2021)

A Comparison of Ambulance Redeployment Systems on Real-World Data. - Niklas Strauß; Max Berrendorf; Tom Haider; Matthias Schubert (ICDM (Workshops), 2022)

Query Embedding on Hyper-Relational Knowledge Graphs. - Dimitrios Alivanistos; Max Berrendorf; Michael Cochez; Mikhail Galkin 001 (ICLR, 2022)

Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract). - Mehdi Ali; Max Berrendorf; Mikhail Galkin 001; Veronika Thost; Tengfei Ma 001; Volker Tresp; Jens Lehmann 001 (IJCAI, 2022)

Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations. - Sandra Gilhuber; Max Berrendorf; Yunpu Ma; Thomas Seidl 001 (IAL@PKDD/ECML, 2022)

Reinforcement Learning for Multi-Agent Stochastic Resource Collection. - Niklas Strauß; David Winkel; Max Berrendorf; Matthias Schubert (ECML/PKDD (4), 2022)

An Open Challenge for Inductive Link Prediction on Knowledge Graphs. - Mikhail Galkin 001; Max Berrendorf; Charles Tapley Hoyt (CoRR, 2022)

A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs. - Charles Tapley Hoyt; Max Berrendorf; Mikhail Galkin 001; Volker Tresp; Benjamin M. Gyori (CoRR, 2022)

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)

Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. - Mehdi Ali; Max Berrendorf; Charles Tapley Hoyt; Laurent Vermue; Mikhail Galkin 001; Sahand Sharifzadeh; Asja Fischer; Volker Tresp; Jens Lehmann 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Machine learning for managing structured and semi-structured data. - Max Berrendorf (2022)

Benchmark Datasets for Inductive Entity Alignment. - Daniel Obraczka; Max Berrendorf; Charles Tapley Hoyt; Michael Galkin; Erhard Rahm (Zenodo, 2022)

Benchmark Datasets for Inductive Entity Alignment. - Daniel Obraczka; Max Berrendorf; Charles Tapley Hoyt; Michael Galkin; Erhard Rahm (Zenodo, 2022)

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

Approximate Answering of Graph Queries. - Michael Cochez; Dimitrios Alivanistos; Erik Arakelyan; Max Berrendorf; Daniel Daza; Mikhail Galkin 001; Pasquale Minervini; Mathias Niepert; Hongyu Ren (CoRR, 2023)

Approximate Answering of Graph Queries. - Michael Cochez; Dimitrios Alivanistos; Erik Arakelyan; Max Berrendorf; Daniel Daza; Mikhail Galkin 001; Pasquale Minervini; Mathias Niepert; Hongyu Ren (Compendium of Neurosymbolic Artificial Intelligence, 2023)