3 papers
stat.ML2026
GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection
Wonmo Koo, Jaeyeong Lee, Taeseong Yoon +1
Multivariate time series anomaly detection (MTAD) is crucial for ensuring the safe and reliable operation of complex systems. Many existing methods learn normal patterns by trainin…
cs.LG2026
Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes
Jaeyeong Lee, Taeseong Yoon, Wonmo Koo +1
Industrial processes often generate complex, interdependent time-series data from multiple sensors across multiple stages, forming complex dependencies among variables and process…
stat.ML2026
Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance
Heegeon Yoon, Heeyoung Kim
When modeling class-imbalanced data, it is crucial to address the imbalance, as models trained on such data tend to be biased towards the majority classes. This problem is amplifie…