1 citations · 1 across the 3 of their papers we have counts for
9 papers
Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors
Oliver Hennhöfer, Christine Preisach
The need for uncertainty quantification in anomaly detection systems has become increasingly important. In this context, effectively controlling Type I error rates without inflatin…
Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with nonconform
Oliver Hennhöfer, Oliver Hennhöfer, Maximilian Kirsch +1
Most anomaly detection systems output scores rather than calibrated decisions, leaving practitioners to choose thresholds heuristically and without clear statistical interpretation…
Between Resolution Collapse and Variance Inflation: Weighted Conformal Anomaly Detection in Low-Data Regimes
Oliver Hennhöfer, Christine Preisach
Standard conformal anomaly detection provides marginal finite-sample guarantees under the assumption of exchangeability . However, real-world data often exhibit distribution shifts…
Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings
Yahya Badran, Christine Preisach
Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content. Many KT models rely on knowledge concepts (KCs)…
Representation Learning of Auxiliary Concepts for Improved Student Modeling and Exercise Recommendation
Yahya Badran, Christine Preisach
Personalized recommendation is a key feature of intelligent tutoring systems, typically relying on accurate models of student knowledge. Knowledge Tracing (KT) models enable this b…
Early Detection of Forest Calamities in Homogeneous Stands -- Deep Learning Applied to Bark-Beetle Outbreaks
Maximilian Kirsch, Jakob Wernicke, Pawan Datta +1
Climate change has increased the vulnerability of forests to insect-related damage, resulting in widespread forest loss in Central Europe and highlighting the need for effective, c…