Maximilian Stubbemann

According to our database, Maximilian Stubbemann authored at least 18 papers between 2019 and 2025.

Collaborative distances:

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

2025

STADE: Standard Deviation as a Pruning Metric.

Diego Coello de Portugal Mecke, Haya Alyoussef, Ilia Koloiarov, Maximilian Stubbemann, Lars Schmidt-Thieme

CoRR, March, 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, February, 2025

Moco: A Learnable Meta Optimizer for Combinatorial Optimization.

Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2025

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research.

Tobias Hille, Maximilian Stubbemann, Tom Hanika

Trans. Mach. Learn. Res., 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

Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting.

Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2024

A Cross-Domain Benchmark for Active Learning.

Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing.

Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme

Proceedings of the IEEE International Conference on Big Data, 2024

2023

Orometry, intrinsic dimensionality and learning: Novel insights into network data.

Maximilian Stubbemann

PhD thesis, 2023

Intrinsic Dimension for Large-Scale Geometric Learning.

Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider

Trans. Mach. Learn. Res., 2023

Selecting Features by their Resilience to the Curse of Dimensionality.

Maximilian Stubbemann, Tobias Hille, Tom Hanika

CoRR, 2023

The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks.

Maximilian Stubbemann, Gerd Stumme

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022

LG4AV: Combining Language Models and Graph Neural Networks for Author Verification.

Maximilian Stubbemann, Gerd Stumme

Proceedings of the Advances in Intelligent Data Analysis XX, 2022

FCA2VEC: Embedding Techniques for Formal Concept Analysis.

Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann

Proceedings of the Complex Data Analytics with Formal Concept Analysis, 2022

2021

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

2020

Orometric Methods in Bounded Metric Data.

Maximilian Stubbemann, Tom Hanika, Gerd Stumme

Proceedings of the Advances in Intelligent Data Analysis XVIII, 2020

2019

FCA2VEC: Embedding Techniques for Formal Concept Analysis.

Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann

CoRR, 2019