9 papers
T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation
Huy Truong, Alexander Lazovik, Victoria Degeler
Graph Neural Networks (GNNs) deployed in real-world systems typically have fixed weights, often leading to degraded performance under distribution shifts. This issue can be mitigat…
Tabular Foundation Models Can Learn Association Rules
Erkan Karabulut, Daniel Daza, Paul Groth +2
Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequen…
Discovering Association Rules in High-Dimensional Small Tabular Data
Erkan Karabulut, Daniel Daza, Paul Groth +1
Association Rule Mining (ARM) aims to discover patterns between features in datasets in the form of propositional rules, supporting both knowledge discovery and interpretable machi…
Neurosymbolic Association Rule Mining from Tabular Data
Erkan Karabulut, Paul Groth, Victoria Degeler
Association Rule Mining (ARM) is the task of mining patterns among data features in the form of logical rules, with applications across a myriad of domains. However, high-dimension…
A Multivariate Statistical Framework for Detection, Classification and Pre-localization of Anomalies in Water Distribution Networks
Oleg Melnikov, Yurii Dorofieiev, Yurii Shakhnovskiy +2
This paper presents a unified framework, for the detection, classification, and preliminary localization of anomalies in water distribution networks using multivariate statistical…
Learning Semantic Association Rules from Internet of Things Data
Erkan Karabulut, Paul Groth, Victoria Degeler
Association Rule Mining (ARM) is the task of discovering commonalities in data in the form of logical implications. ARM is used in the Internet of Things (IoT) for different tasks…