Cloud-Enabled High-Dimensional Low-Sample Size Machine Learning: Sparse Precision Matrix Estimation

This project aims to develop a scalable and efficient cloud-based method for sparse precision matrix estimation, a crucial task in the increasingly prevalent field of high-dimensional, low-sample-size (HDLSS) machine learning. If successful, this project will establish the foundation for standard or low-power systems to perform computationally demanding HDLSS machine learning and data analytics tasks common in many applications. Our project faces two key challenges: (C1) a hyperparameter tuning process that demands specialized domain expertise, and (C2) the high computational costs of solution methods. To overcome these, we require (R1) the elimination of the time-consuming process required for hyperparameter tuning, and (R2) an efficient and performant cloud-based solution that seamlessly integrates into existing workflows. Our objectives are (O1) to establish an algorithm that eliminates the need for hyperparameter tuning (i.e., tuning-free hyperparameters) and (O2) to develop a cloud-based solution with an API that leverages structural attributes of the computation for performance and scalability.