3 citations · 8 across the 19 of their papers we have counts for
4 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…
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…