Quantile regression is a method of fundamental importance. How to efficiently conduct quantile regression for a large dataset on a distributed system is of great importance. We show that the popula…
Modern statistical analysis often encounters massive datasets with ultrahigh-dimensional features. In this work, we develop a subsampling approach for feature screening with massive datasets. The a…
We study a server routing-scheduling problem in a distributed queueing system, where the system consists of multiple queues at different locations. In a distributed queueing system, servers are sha…
Real-time and high-speed interrogation of optical fiber sensors normally requires sophisticated detection systems. Here a novel microwave photonic approach for interrogation of high-speed and high-…
We use laboratory experiments to evaluate the effects of cognitive stress on inventory management decisions in a finite horizon economic order quantity (EOQ) model. We manipulate two sources of cog…
In this paper, we demonstrated a high-brightness bidirectional tandem-pumped confined-doped fiber amplifier with nearly 8 kW output power. The technical difficulties for realizing the bidirectional…
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, communi…
This article investigates deep neural networks (DNNs)-based lung nodule classification with hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally expensive problem, …
One-shot neural architecture search (NAS) has recently become mainstream in the NAS community because it significantly improves computational efficiency through weight sharing. However, the superne…
This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been intro…