The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-
intensive applications such as ETL, query processing, or machine learning (ML). Several …

Machine learning can provide deep insights into data, allowing machines to make high-
quality predictions and having been widely used in real-world applications, such as text …

With the ever-evolving concerns on privacy protection, vertical federated learning (FL),
where participants own non-overlap** features for the same set of instances, is becoming …

Due to the rising concerns on privacy protection, how to build machine learning (ML) models
over different data sources with security guarantees is gaining more popularity. Vertical …

Abstract The application of Deep Neural Networks (DNNs) for monitoring cyberattacks in
Internet of Things (IoT) systems has gained significant attention in recent years. However …

Federated Learning (FL) has been an emerging trend in machine learning and artificial
intelligence. It allows multiple participants to collaboratively train a better global model and …

Graph Convolutional Network (GCN) is a widely used method for learning from graph-based
data. However, it fails to use the unlabeled data to its full potential, thereby hindering its …

Abstract The Gradient Boosting Decision Tree (GBDT) is a popular machine learning model
for various tasks in recent years. In this paper, we study how to improve model accuracy of …

Recent years have witnessed significant success in Gradient Boosting Decision Trees
(GBDT) for a wide range of machine learning applications. Generally, a consensus about …

Abstract Gradient Boosting Regression (GBR) models are widely used and can give
effective results in regression and classification problems. The main value of the approach …