Calibration of Galvanometric Laser Scanners Using Statistical Learning Methods. - Stefan Lüdtke; Benjamin Wagner 002; Ralf Bruder; Patrick Stüber; Floris Ernst; Achim Schweikard; Tobias Wissel (Bildverarbeitung für die Medizin, 2015)

Abstracting from Observation-Equivalent Entities in Human Behavior Modeling. - Max Schröder; Stefan Lüdtke; Sebastian Bader 001; Frank Krüger 001; Thomas Kirste (AAAI Workshops, 2017)

Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer' s Disease. - Stefan Lüdtke; Albert Hein; Frank Krüger 001; Sebastian Bader 001; Thomas Kirste (BIOSIGNALS, 2017)

Where are my colleagues?: Tracking and Counting Multiple Persons using Lifted Marginal Filtering. - Stefan Lüdtke; Max Schröder; Frank Krüger 001; Thomas Kirste (iWOAR, 2017)

LiMa: Sequential Lifted Marginal Filtering on Multiset State Descriptions. - Max Schröder; Stefan Lüdtke; Sebastian Bader 001; Frank Krüger 001; Thomas Kirste (KI, 2017)

Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems. - Max Schröder; Stefan Lüdtke; Sebastian Bader 001; Frank Krüger 001; Thomas Kirste (CoRR, 2017)

Lifted Filtering via Exchangeable Decomposition. - Stefan Lüdtke; Max Schröder; Sebastian Bader 001; Kristian Kersting; Thomas Kirste (IJCAI, 2018)

Gaussian Lifted Marginal Filtering. - Stefan Lüdtke; Alejandro Molina 001; Thomas Kirste (iWOAR, 2018)

Approximate Probabilistic Parallel Multiset Rewriting Using MCMC. - Stefan Lüdtke; Max Schröder; Thomas Kirste (KI, 2018)

Evaluation of cupboard door sensors for improving activity recognition in the kitchen. - Samuel Whitehouse; Kristina Y. Yordanova; Stefan Lüdtke; Adeline Paiement; Majid Mirmehdi (PerCom Workshops, 2018)

Flood loss estimation using 3D city models and remote sensing data. - Kai Schröter; Stefan Lüdtke; Richard Redweik; Jessica Meier; Mathias Bochow; Lutz Ross; Claus Nagel; Heidi Kreibich (Environ. Model. Softw., 2018)

State-Space Abstractions for Probabilistic Inference: A Systematic Review. - Stefan Lüdtke; Max Schröder; Frank Krüger 001; Sebastian Bader 001; Thomas Kirste (J. Artif. Intell. Res., 2018)

Gaussian Lifted Marginal Filtering. - Stefan Lüdtke; Alejandro Molina 001; Kristian Kersting; Thomas Kirste (KI, 2019)

Human Activity and Context Recognition using Lifted Marginal Filtering. - Stefan Lüdtke; Kristina Y. Yordanova; Thomas Kirste (PerCom Workshops, 2019)

Combining Symbolic Reasoning and Deep Learning for Human Activity Recognition. - Fernando Moya Rueda; Stefan Lüdtke; Max Schröder; Kristina Y. Yordanova; Thomas Kirste; Gernot A. Fink (PerCom Workshops, 2019)

On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition. - Stefan Lüdtke; Maximilian Popko; Thomas Kirste (Künstliche Intell., 2019)

Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring. - Kristina Y. Yordanova; Stefan Lüdtke; Samuel Whitehouse; Frank Krüger 001; Adeline Paiement; Majid Mirmehdi; Ian Craddock; Thomas Kirste (Sensors, 2019)

Lifted Marginal Filtering for Asymmetric Models by Clustering-Based Merging. - Stefan Lüdtke; Marcel Gehrke; Tanya Braun; Ralf Möller 001; Thomas Kirste (ECAI, 2020)

Measuring motion behavior to detect spatial disorientation in a VR environment. - Stefan Lüdtke; Chimezie O. Amaefule; Thomas Kirste; Stefan J. Teipel (PETRA, 2020)

Lifted Bayesian Filtering in Multiset Rewriting Systems. - Stefan Lüdtke; Thomas Kirste (J. Artif. Intell. Res., 2020)

Lifted Bayesian filtering in multi-entity systems - Stefan Lüdtke (2020)

Human Activity Recognition using Attribute-Based Neural Networks and Context Information. - Stefan Lüdtke; Fernando Moya Rueda; Waqas Ahmed; Gernot A. Fink; Thomas Kirste (CoRR, 2021)

Self-learning Governance of Black-Box Multi-Agent Systems. - Michael Oesterle; Christian Bartelt; Stefan Lüdtke; Heiner Stuckenschmidt (COINE, 2022)

Exchangeability-Aware Sum-Product Networks. - Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (IJCAI, 2022)

Discovering Behavioural Predispositions in Data to Improve Human Activity Recognition. - Maximilian Popko; Sebastian Bader 001; Stefan Lüdtke; Thomas Kirste (iWOAR, 2022)

Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs. - Timon Felske; Stefan Lüdtke; Sebastian Bader 001; Thomas Kirste (CoRR, 2022)

Outlier Explanation via Sum-Product Networks. - Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (CoRR, 2022)

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition. - Maximilian Popko; Sebastian Bader 001; Stefan Lüdtke; Thomas Kirste (CoRR, 2022)

Context-Aware Human Activity Recognition in Industrial Processes. - Friedrich Niemann; Stefan Lüdtke; Christian Bartelt; Michael ten Hompel (Sensors, 2022)

Online Random Feature Forests for Learning in Varying Feature Spaces. - Christian Schreckenberger; Yi He 007; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (AAAI, 2023)

Outlying Aspect Mining via Sum-Product Networks. - Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (PAKDD (1), 2023)

Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition. - Nils Wilken; Lea Cohausz; Johannes Schaum; Stefan Lüdtke; Heiner Stuckenschmidt (CoRR, 2023)

Leveraging Planning Landmarks for Hybrid Online Goal Recognition. - Nils Wilken; Lea Cohausz; Johannes Schaum; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (CoRR, 2023)

Learning Decision Trees with Gradient Descent. - Sascha Marton; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (CoRR, 2023)

Towards Machine Learning-based Fish Stock Assessment. - Stefan Lüdtke; Maria E. Pierce (CoRR, 2023)

On the Aggregation of Rules for Knowledge Graph Completion. - Patrick Betz; Stefan Lüdtke; Christian Meilicke; Heiner Stuckenschmidt (CoRR, 2023)

GRANDE: Gradient-Based Decision Tree Ensembles. - Sascha Marton; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (CoRR, 2023)

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent. - Sascha Marton; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (AAAI, 2024)

GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data. - Sascha Marton; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (ICLR, 2024)

Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods. - Andrej Tschalzev; Paul Nitschke; Lukas Kirchdorfer; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (IJCAI, 2024)

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data. - Andrej Tschalzev; Sascha Marton; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (NeurIPS, 2024)

Rule Confidence Aggregation for Knowledge Graph Completion. - Patrick Betz; Stefan Lüdtke; Christian Meilicke; Heiner Stuckenschmidt (RuleML+RR, 2024)

SYMPOL: Symbolic Tree-Based On-Policy Reinforcement Learning. - Sascha Marton; Tim Grams; Florian Vogt; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (CoRR, 2024)

Explaining neural networks without access to training data. - Sascha Marton; Stefan Lüdtke; Christian Bartelt; Andrej Tschalzev; Heiner Stuckenschmidt (Mach. Learn., 2024)

Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization. - Sascha Marton; Tim Grams; Florian Vogt; Stefan Lüdtke; Christian Bartelt; Heiner Stuckenschmidt (ICLR, 2025)

Unreflected Use of Tabular Data Repositories Can Undermine Research Quality. - Andrej Tschalzev; Lennart Purucker; Stefan Lüdtke; Frank Hutter; Christian Bartelt; Heiner Stuckenschmidt (CoRR, 2025)

Informed, but Not Always Improved: Challenging the Benefit of Background Knowledge in GNNs. - Kutalmis Coskun; Ivo Kavisanczki; Amin Mirzaei; Tom Siegl; Bjarne C. Hiller; Stefan Lüdtke; Martin Becker (CoRR, 2025)