Nowadays, robots are dominating the manufacturing, entertainment, and healthcare
industries. Robot vision aims to equip robots with the capabilities to discover information …

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …

In recent years, studies related to topic derivation in Twitter have gained a lot of interest from
businesses and academics. The interconnection between users and information has made …

Time series prediction problems are becoming increasingly high-dimensional in modern
applications, such as climatology and demand forecasting. For example, in the latter …

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

Low rank matrix completion plays a fundamental role in collaborative filtering applications,
the key idea being that the variables lie in a smaller subspace than the ambient space …

Time series forecasting and spatiotemporal kriging are the two most important tasks in
spatiotemporal data analysis. Recent research on graph neural networks has made …

Location recommendation is an important means to help people discover attractive
locations. However, extreme sparsity of user-location matrices leads to a severe challenge …

Many problem settings in machine learning are concerned with the simultaneous prediction
of multiple target variables of diverse type. Amongst others, such problem settings arise in …

Missingness and corruption are common problems for real-world traffic data. How to
accurately perform imputation and prediction based on incomplete or even sparse traffic …