31 citations · 61 across the 22 of their papers we have counts for
7 papers · 1 filter
GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
Sascha Marton, Stefan Lüdtke, Christian Bartelt +1
Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, t…
On the Aggregation of Rules for Knowledge Graph Completion
Patrick Betz, Stefan Lüdtke, Christian Meilicke +1
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding…
Learning Disentangled Discrete Representations
David Friede, Christian Reimers, Heiner Stuckenschmidt +1
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations…
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?
Nils Wilken, Lea Cohausz, Christian Bartelt +1
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios, it is im…
GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
Sascha Marton, Stefan Lüdtke, Christian Bartelt +1
Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difficult optimization pr…
Leveraging Planning Landmarks for Hybrid Online Goal Recognition
Nils Wilken, Lea Cohausz, Johannes Schaum +3
Goal recognition is an important problem in many application domains (e.g., pervasive computing, intrusion detection, computer games, etc.). In many application scenarios it is imp…
Investigating the Combination of Planning-Based and Data-Driven Methods for Goal Recognition
Nils Wilken, Lea Cohausz, Johannes Schaum +2
An important feature of pervasive, intelligent assistance systems is the ability to dynamically adapt to the current needs of their users. Hence, it is critical for such systems to…