Christian Schreckenberger — accepted AI-conference papers

2023

Online Random Feature Forests for Learning in Varying Feature Spaces

Christian Schreckenberger, Yi He, Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt

AAAI 2023technical

In this paper, we propose a new online learning algorithm tailored for data streams described by varying feature spaces (VFS), wherein new features constantly emerge and old features may stop to be observed over various time spans. Our proposed algorithm, named Online Random Feature Forests for Feat…

Cited by 14 Source ↗PDF ↗Scholar ↗

2023

Towards Utilitarian Online Learning -- A Review of Online Algorithms in Open Feature Space

Yi He, Christian Schreckenberger, Heiner Stuckenschmidt, Xindong Wu

IJCAI 2023poster

Human intelligence comes from the capability to describe and make sense of the world surrounding us, often in a lifelong manner. Online Learning (OL) allows a model to simulate this capability, which involves processing data in sequence, making predictions, and learning from predictive errors. Howev…

Cited by 7 Source ↗PDF ↗Scholar ↗