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ORCIDStefan Lüdtke
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2020 – today
- 2024
[j7]Sascha Marton
, Stefan Lüdtke, Christian Bartelt, Andrej Tschalzev, Heiner Stuckenschmidt:
Explaining neural networks without access to training data. Mach. Learn. 113(6): 3633-3652 (2024)- [c23]Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent. AAAI 2024: 14323-14331 - [c22]Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data. ICLR 2024 - [c21]Andrej Tschalzev, Paul Nitschke, Lukas Kirchdorfer, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods. IJCAI 2024: 5018-5026 - [c20]Patrick Betz, Stefan Lüdtke, Christian Meilicke, Heiner Stuckenschmidt:
Rule Confidence Aggregation for Knowledge Graph Completion. RuleML+RR 2024: 32-49 - [i18]Andrej Tschalzev, Paul Nitschke, Lukas Kirchdorfer, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods. CoRR abs/2407.01115 (2024) - [i17]Andrej Tschalzev, Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data. CoRR abs/2407.02112 (2024) - [i16]Sascha Marton, Tim Grams, Florian Vogt, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
SYMPOL: Symbolic Tree-Based On-Policy Reinforcement Learning. CoRR abs/2408.08761 (2024) - 2023
- [c19]Christian Schreckenberger, Yi He, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Online Random Feature Forests for Learning in Varying Feature Spaces. AAAI 2023: 4587-4595 - [c18]Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Outlying Aspect Mining via Sum-Product Networks. PAKDD (1) 2023: 27-38 - [i15]Nils Wilken, Lea Cohausz, Johannes Schaum, Stefan Lüdtke, Heiner Stuckenschmidt:
Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition. CoRR abs/2301.05608 (2023) - [i14]Nils Wilken, Lea Cohausz, Johannes Schaum, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Leveraging Planning Landmarks for Hybrid Online Goal Recognition. CoRR abs/2301.10571 (2023) - [i13]Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Learning Decision Trees with Gradient Descent. CoRR abs/2305.03515 (2023) - [i12]Stefan Lüdtke, Maria E. Pierce:
Towards Machine Learning-based Fish Stock Assessment. CoRR abs/2308.03403 (2023) - [i11]Patrick Betz, Stefan Lüdtke, Christian Meilicke, Heiner Stuckenschmidt:
On the Aggregation of Rules for Knowledge Graph Completion. CoRR abs/2309.00306 (2023) - [i10]Sascha Marton, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
GRANDE: Gradient-Based Decision Tree Ensembles. CoRR abs/2309.17130 (2023) - 2022
- [j6]Friedrich Niemann, Stefan Lüdtke, Christian Bartelt, Michael ten Hompel:
Context-Aware Human Activity Recognition in Industrial Processes. Sensors 22(1): 134 (2022) - [c17]Michael Oesterle, Christian Bartelt, Stefan Lüdtke, Heiner Stuckenschmidt:
Self-learning Governance of Black-Box Multi-Agent Systems. COINE 2022: 73-91 - [c16]Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Exchangeability-Aware Sum-Product Networks. IJCAI 2022: 4864-4870 - [c15]Maximilian Popko, Sebastian Bader, Stefan Lüdtke, Thomas Kirste:
Discovering Behavioural Predispositions in Data to Improve Human Activity Recognition. iWOAR 2022: 3:1-3:7 - [i9]Timon Felske, Stefan Lüdtke, Sebastian Bader, Thomas Kirste:
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs. CoRR abs/2202.00332 (2022) - [i8]Sascha Marton, Stefan Lüdtke, Christian Bartelt, Andrej Tschalzev, Heiner Stuckenschmidt:
Explaining Neural Networks without Access to Training Data. CoRR abs/2206.04891 (2022) - [i7]Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Outlier Explanation via Sum-Product Networks. CoRR abs/2207.08414 (2022) - [i6]Maximilian Popko, Sebastian Bader, Stefan Lüdtke, Thomas Kirste:
Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition. CoRR abs/2207.08816 (2022) - 2021
- [i5]Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt:
Exchangeability-Aware Sum-Product Networks. CoRR abs/2110.05165 (2021) - [i4]Stefan Lüdtke, Fernando Moya Rueda, Waqas Ahmed, Gernot A. Fink, Thomas Kirste:
Human Activity Recognition using Attribute-Based Neural Networks and Context Information. CoRR abs/2111.04564 (2021) - 2020
- [b1]Stefan Lüdtke:
Lifted Bayesian filtering in multi-entity systems. Rostock University, Germany, 2020 - [j5]Stefan Lüdtke, Thomas Kirste:
Lifted Bayesian Filtering in Multiset Rewriting Systems. J. Artif. Intell. Res. 69: 1203-1254 (2020) - [c14]Stefan Lüdtke, Marcel Gehrke, Tanya Braun, Ralf Möller, Thomas Kirste:
Lifted Marginal Filtering for Asymmetric Models by Clustering-Based Merging. ECAI 2020: 2608-2615 - [c13]Stefan Lüdtke, Chimezie O. Amaefule, Thomas Kirste, Stefan J. Teipel:
Measuring motion behavior to detect spatial disorientation in a VR environment. PETRA 2020: 77:1-77:2
2010 – 2019
- 2019
- [j4]Stefan Lüdtke, Maximilian Popko, Thomas Kirste:
On the Applicability of Probabilistic Programming Languages for Causal Activity Recognition. Künstliche Intell. 33(4): 389-399 (2019) - [j3]Kristina Y. Yordanova, Stefan Lüdtke, Samuel Whitehouse, Frank Krüger, Adeline Paiement, Majid Mirmehdi, Ian Craddock, Thomas Kirste:
Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring. Sensors 19(3): 646 (2019) - [c12]Stefan Lüdtke, Alejandro Molina, Kristian Kersting, Thomas Kirste:
Gaussian Lifted Marginal Filtering. KI 2019: 230-243 - [c11]Fernando Moya Rueda, Stefan Lüdtke, Max Schröder, Kristina Y. Yordanova, Thomas Kirste, Gernot A. Fink:
Combining Symbolic Reasoning and Deep Learning for Human Activity Recognition. PerCom Workshops 2019: 22-27 - [c10]Stefan Lüdtke, Kristina Y. Yordanova, Thomas Kirste:
Human Activity and Context Recognition using Lifted Marginal Filtering. PerCom Workshops 2019: 83-88 - 2018
- [j2]Kai Schröter, Stefan Lüdtke, Richard Redweik, Jessica Meier, Mathias Bochow, Lutz Ross, Claus Nagel, Heidi Kreibich:
Flood loss estimation using 3D city models and remote sensing data. Environ. Model. Softw. 105: 118-131 (2018) - [j1]Stefan Lüdtke, Max Schröder, Frank Krüger, Sebastian Bader, Thomas Kirste:
State-Space Abstractions for Probabilistic Inference: A Systematic Review. J. Artif. Intell. Res. 63: 789-848 (2018) - [c9]Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste:
Lifted Filtering via Exchangeable Decomposition. IJCAI 2018: 5067-5073 - [c8]Stefan Lüdtke, Alejandro Molina, Thomas Kirste:
Gaussian Lifted Marginal Filtering. iWOAR 2018: 21:1-21:3 - [c7]Stefan Lüdtke, Max Schröder, Thomas Kirste:
Approximate Probabilistic Parallel Multiset Rewriting Using MCMC. KI 2018: 73-85 - [c6]Samuel Whitehouse, Kristina Y. Yordanova, Stefan Lüdtke, Adeline Paiement, Majid Mirmehdi:
Evaluation of cupboard door sensors for improving activity recognition in the kitchen. PerCom Workshops 2018: 167-172 - [i3]Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste:
Lifted Filtering via Exchangeable Decomposition. CoRR abs/1801.10495 (2018) - [i2]Stefan Lüdtke, Max Schröder, Frank Krüger, Sebastian Bader, Thomas Kirste:
State-Space Abstractions for Probabilistic Inference: A Systematic Review. CoRR abs/1804.06748 (2018) - 2017
- [c5]Max Schröder, Stefan Lüdtke, Sebastian Bader, Frank Krüger, Thomas Kirste:
Abstracting from Observation-Equivalent Entities in Human Behavior Modeling. AAAI Workshops 2017 - [c4]Stefan Lüdtke, Albert Hein, Frank Krüger, Sebastian Bader, Thomas Kirste:
Actigraphic Sleep Detection for Real-World Data of Healthy Young Adults and People with Alzheimer' s Disease. BIOSIGNALS 2017: 185-192 - [c3]Stefan Lüdtke, Max Schröder, Frank Krüger, Thomas Kirste:
Where are my colleagues?: Tracking and Counting Multiple Persons using Lifted Marginal Filtering. iWOAR 2017: 6:1-6:6 - [c2]Max Schröder, Stefan Lüdtke, Sebastian Bader, Frank Krüger, Thomas Kirste:
LiMa: Sequential Lifted Marginal Filtering on Multiset State Descriptions. KI 2017: 222-235 - [i1]Max Schröder, Stefan Lüdtke, Sebastian Bader, Frank Krüger, Thomas Kirste:
Sequential Lifted Bayesian Filtering in Multiset Rewriting Systems. CoRR abs/1707.06446 (2017) - 2015
- [c1]Stefan Lüdtke, Benjamin Wagner, Ralf Bruder, Patrick Stüber, Floris Ernst, Achim Schweikard, Tobias Wissel:
Calibration of Galvanometric Laser Scanners Using Statistical Learning Methods. Bildverarbeitung für die Medizin 2015: 467-472
Coauthor Index
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