3 citations · 4 across the 9 of their papers we have counts for
10 papers
Exploring Interpretability in Deep Learning-Based Forest Classification - Separating Similar Forest Classes using Sentinel-2 Time Series
Maximilian Kirsch, Christine Preisach
The ecological insurance theory posits that diverse forests are more resilient and better able to adapt to changing environmental conditions. Increasing drought and heat stress in…
Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with nonconform
Oliver Hennhöfer, Maximilian Kirsch, Christine Preisach
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…
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…
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)…
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…
Sparse Binary Representation Learning for Knowledge Tracing
Yahya Badran, Christine Preisach
Knowledge tracing (KT) models aim to predict students' future performance based on their historical interactions. Most existing KT models rely exclusively on human-defined knowledg…
Supervised Transfer Learning Framework for Fault Diagnosis in Wind Turbines
Kenan Weber, Christine Preisach
Common challenges in fault diagnosis include the lack of labeled data and the need to build models for each domain, resulting in many models that require supervision. Transfer lear…
Addressing Label Leakage in Knowledge Tracing Models
Yahya Badran, Christine Preisach
Knowledge Tracing (KT) is concerned with predicting students' future performance on learning items in intelligent tutoring systems. Learning items are tagged with skill labels call…
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…