Driven by the rapid progress in quantum hardware, recent years have witnessed a furious
race for quantum technologies in both academia and industry. Universal quantum …

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

We are living in an era of big data, where the process of generating data is continuously
been taking place with each coming second. Data that is more varied and extremely …

Today's artificial intelligence still faces two major challenges. One is that, in most industries,
data exists in the form of isolated islands. The other is the strengthening of data privacy and …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

We introduce a new and increasingly relevant setting for distributed optimization in machine
learning, where the data defining the optimization are unevenly distributed over an …

Parallel implementations of stochastic gradient descent (SGD) have received significant
research attention, thanks to its excellent scalability properties. A fundamental barrier when …

Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

In the current world of the Internet of Things, cyberspace, mobile devices, businesses, social
media platforms, healthcare systems, etc., there is a lot of data online today. Machine …

Tree boosting is a highly effective and widely used machine learning method. In this paper,
we describe a scalable end-to-end tree boosting system called XGBoost, which is used …