Maximilian Stubbemann

32 papers A* 2A 1B 3Misc 1Journal 19Unranked 4
YearRankTypeTitle / Venue / Authors
2025 — conf
Attribute-Aware Sequential Recommendation Model for Used Car Auctions.
ECML/PKDD (9)
Shereen Elsayed, Ngoc Son Le, Ahmed Rashed, Lukas Hestermeyer, Radoslaw Wlodarczyk, Maximilian Stubbemann, Lars Schmidt-Thieme
2025 J jnl
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
CoRR
Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi, Maximilian Stubbemann, Lars Schmidt-Thieme
2025 — conf
Moco: A Learnable Meta Optimizer for Combinatorial Optimization.
PAKDD (3)
Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme
2025 J jnl
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs.
CoRR
Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme
2025 A* conf
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs.
ICLR
Christian Klötergens, Vijaya Krishna Yalavarthi, Randolf Scholz, Maximilian Stubbemann, Stefan Born, Lars Schmidt-Thieme
2025 J jnl
STADE: Standard Deviation as a Pruning Metric.
CoRR
Diego Coello de Portugal Mecke, Haya Alyoussef, Ilia Koloiarov, Maximilian Stubbemann, Lars Schmidt-Thieme
2025 A conf
TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression.
ECAI
Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag, Maximilian Stubbemann, Lars Schmidt-Thieme
2025 J jnl
TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression.
CoRR
Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Jonas Sonntag, Maximilian Stubbemann, Lars Schmidt-Thieme
2025 Misc conf
Unsupervised Selection of Features by Their Resilience to the Curse of Dimensionality.
KI
Maximilian Stubbemann, Tobias Hille, Tom Hanika
2024 A* conf
A Cross-Domain Benchmark for Active Learning.
NeurIPS
Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 J jnl
A Cross-Domain Benchmark for Active Learning.
CoRR
Thorben Werner, Johannes Burchert, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 J jnl
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training.
CoRR
Johannes Burchert, Thorben Werner, Vijaya Krishna Yalavarthi, Diego Coello de Portugal, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 — conf
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting.
ECML/PKDD (4)
Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 J jnl
Functional Latent Dynamics for Irregularly Sampled Time Series Forecasting.
CoRR
Christian Klötergens, Vijaya Krishna Yalavarthi, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 J jnl
Moco: A Learnable Meta Optimizer for Combinatorial Optimization.
CoRR
Tim Dernedde, Daniela Thyssens, Sören Dittrich, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 B conf
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing.
IEEE Big Data
Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 J jnl
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing.
CoRR
Kiran Madhusudhanan, Gunnar Behrens, Maximilian Stubbemann, Lars Schmidt-Thieme
2024 J jnl
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research.
CoRR
Tobias Hille, Maximilian Stubbemann, Tom Hanika
2024 J jnl
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research.
Trans. Mach. Learn. Res.
Tobias Hille, Maximilian Stubbemann, Tom Hanika
2023 J jnl
Intrinsic Dimension for Large-Scale Geometric Learning.
Trans. Mach. Learn. Res.
Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
2023 — —
Orometry, intrinsic dimensionality and learning: Novel insights into network data.
Maximilian Stubbemann
2023 J jnl
Selecting Features by their Resilience to the Curse of Dimensionality.
CoRR
Maximilian Stubbemann, Tobias Hille, Tom Hanika
2023 — conf
The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks.
ECML/PKDD (3)
Maximilian Stubbemann, Gerd Stumme
2022 — ch.
FCA2VEC: Embedding Techniques for Formal Concept Analysis.
Complex Data Analytics with Formal Concept Analysis
Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann
2022 J jnl
Intrinsic Dimension for Large-Scale Geometric Learning.
CoRR
Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
2022 B conf
LG4AV: Combining Language Models and Graph Neural Networks for Author Verification.
IDA
Maximilian Stubbemann, Gerd Stumme
2021 J jnl
LG4AV: Combining Language Models and Graph Neural Networks for Author Verification.
CoRR
Maximilian Stubbemann, Gerd Stumme
2021 J jnl
Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research.
Scientometrics
Tobias Koopmann, Maximilian Stubbemann, Matthias Kapa, Michael Paris, Guido Buenstorf, Tom Hanika, Andreas Hotho, Robert Jäschke, Gerd Stumme
2021 J jnl
The Mont Blanc of Twitter: Identifying Hierarchies of Outstanding Peaks in Social Networks.
CoRR
Maximilian Stubbemann, Gerd Stumme
2020 B conf
Orometric Methods in Bounded Metric Data.
IDA
Maximilian Stubbemann, Tom Hanika, Gerd Stumme
2019 J jnl
FCA2VEC: Embedding Techniques for Formal Concept Analysis.
CoRR
Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann
2019 J jnl
Orometric Methods in Bounded Metric Data.
CoRR
Maximilian Stubbemann, Tom Hanika, Gerd Stumme