most citedStochastic tensor space feature theory with applications to robust machine learning

1 citations · 1 across the 1 of their papers we have counts for

collaborators

2 papers

stat.ML2026★ 1 cited

Stochastic tensor space feature theory with applications to robust machine learning

Julio Enrique Castrillon-Candas, Kaili Shi, Dingning Liu +4

In this paper we develop a Multilevel Orthogonal Subspace (MOS) Karhunen-Loeve feature theory based on stochastic tensor spaces, for the construction of robust machine learning fea…

stat.ML2026

Distribution-Free Stochastic Analysis and Robust Multilevel Vector Field Anomaly Detection

Julio E Castrillon-Candas, Michael Rosenbaum, Mark Kon

Massive vector field datasets are common in multi-spectral optical and radar sensors, among many other emerging areas of application. We develop a novel stochastic functional (data…