In this overview paper, data-driven learning model-based cooperative localization and
location data processing are considered, in line with the emerging machine learning and big …

In large-scale distributed machine learning (DML), the synchronization efficiency of the
distributed algorithm becomes a critical factor that affects the training time of machine …

Adaptive trust-region methods attempt to maintain strong convergence guarantees without
depending on conservative estimates of problem properties such as Lipschitz constants …

This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-
cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) …

Scenario‐based stochastic optimal control problems suffer from the curse of dimensionality
as they can easily grow to six and seven figure sizes. First‐order methods are suitable as …

Executing the resource-consuming parallel model, Earth System Models (ESMs), on the
many-core CPU clusters can effectively improve the performance due to the clusters' higher …

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the
highest among hospital admissions in the US. Improved understanding of disease states …

Neural Networks (NNs) are getting deeper and more complicated to the point where single
accelerator training is no longer an option. Training today's state-of-the-art NNs is done in …

The increasing volume and availability of Electronic Health Records (EHRs) open up
opportunities for computational models to improve patient care. Key factors in improving …

We study an efficient algorithm to solve the distributionally robust optimization (DRO)
problem, which has recently attracted attention as a new paradigm for decision making in …