Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
LUCoS: Latent Unsupervised Context Selection for Tabular Foundation Models
Oroel Ipas, Guillermo Gomez-Trenado, Rocío Romero-Zaliz +1
Selecting which instances to label is a key challenge in low-label tabular learning. For recent Tabular Foundation Models such as TabPFN, context selection directly determines pred…
cs.LG2026
VACE: Learning Geometrically Structured Representations for Time Series Anomaly Detection
Alberto D. Cencillo, Leonardo Concepción, Isaac Triguero +1
Anomaly detection in multivariate time series is a critical task across a wide range of real-world applications, where abnormal behaviour is rare, labels are unavailable, and the c…