22 citations · 78 across the 23 of their papers we have counts for
8 papers · 1 filter
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…
Multilevel Stochastic Optimization for Imputation in Massive Medical Data Records
Wenrui Li, Xiaoyu Wang, Yuetian Sun +3
It has long been a recognized problem that many datasets contain significant levels of missing numerical data. A potentially critical predicate for application of machine learning…
Stochastic tensor space feature theory with applications to robust machine learning
Julio Enrique Castrillon-Candas, Kaili Shi, Dingning Liu +5
In this paper we develop a Multilevel Orthogonal Subspace (MOS) Karhunen--Loève feature theory based on stochastic tensor spaces, for the construction of robust machine learning fe…
A complexity analysis of statistical learning algorithms
Mark A. Kon
We apply information-based complexity analysis to support vector machine (SVM) algorithms, with the goal of a comprehensive continuous algorithmic analysis of such algorithms. This…
On the probabilistic continuous complexity conjecture
Mark A. Kon
In this paper we prove the probabilistic continuous complexity conjecture. In continuous complexity theory, this states that the complexity of solving a continuous problem with pro…
On Some Integrated Approaches to Inference
Mark A. Kon, Leszek Plaskota
We present arguments for the formulation of unified approach to different standard continuous inference methods from partial information. It is claimed that an explicit partition o…