FCA2VEC: Embedding Techniques for Formal Concept Analysis. - Dominik Dürrschnabel; Tom Hanika; Maximilian Stubbemann (CoRR, 2019)

Orometric Methods in Bounded Metric Data. - Maximilian Stubbemann; Tom Hanika; Gerd Stumme (IDA, 2020)

Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research. - Tobias Koopmann; Maximilian Stubbemann; Matthias Kapa; Michael Paris; Guido Buenstorf; Tom Hanika; Andreas Hotho; Robert Jäschke; Gerd Stumme (Scientometrics, 2021)

FCA2VEC: Embedding Techniques for Formal Concept Analysis. - Dominik Dürrschnabel; Tom Hanika; Maximilian Stubbemann (Complex Data Analytics with Formal Concept Analysis, 2022)

LG4AV: Combining Language Models and Graph Neural Networks for Author Verification. - Maximilian Stubbemann; Gerd Stumme (IDA, 2022)

The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks. - Maximilian Stubbemann; Gerd Stumme (ECML/PKDD (3), 2023)

Selecting Features by their Resilience to the Curse of Dimensionality. - Maximilian Stubbemann; Tobias Hille; Tom Hanika (CoRR, 2023)

Intrinsic Dimension for Large-Scale Geometric Learning. - Maximilian Stubbemann; Tom Hanika; Friedrich Martin Schneider (Trans. Mach. Learn. Res., 2023)

Orometry, intrinsic dimensionality and learning: Novel insights into network data. - Maximilian Stubbemann (2023)

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing. - Kiran Madhusudhanan; Gunnar Behrens; Maximilian Stubbemann; Lars Schmidt-Thieme (IEEE Big Data, 2024)

A Cross-Domain Benchmark for Active Learning. - Thorben Werner; Johannes Burchert; Maximilian Stubbemann; Lars Schmidt-Thieme (NeurIPS, 2024)

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting. - Christian Klötergens; Vijaya Krishna Yalavarthi; Maximilian Stubbemann; Lars Schmidt-Thieme (ECML/PKDD (4), 2024)

Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training. - Johannes Burchert; Thorben Werner; Vijaya Krishna Yalavarthi; Diego Coello de Portugal; Maximilian Stubbemann; Lars Schmidt-Thieme (CoRR, 2024)

Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research. - Tobias Hille; Maximilian Stubbemann; Tom Hanika (Trans. Mach. Learn. Res., 2024)

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs. - Christian Klötergens; Vijaya Krishna Yalavarthi; Randolf Scholz; Maximilian Stubbemann; Stefan Born; Lars Schmidt-Thieme (ICLR, 2025)

Moco: A Learnable Meta Optimizer for Combinatorial Optimization. - Tim Dernedde; Daniela Thyssens; Sören Dittrich; Maximilian Stubbemann; Lars Schmidt-Thieme (PAKDD (3), 2025)

Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting? - Ibram Abdelmalak; Kiran Madhusudhanan; Jungmin Choi; Maximilian Stubbemann; Lars Schmidt-Thieme (CoRR, 2025)

STADE: Standard Deviation as a Pruning Metric. - Diego Coello de Portugal Mecke; Haya Alyoussef; Ilia Koloiarov; Maximilian Stubbemann; Lars Schmidt-Thieme (CoRR, 2025)