Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Temporal data representing chronological observations of complex systems can be
ubiquitously collected in smart industry, medicine, finance and etc. In the last decade, many …

Theoretically, the Markov boundary (MB) is the optimal solution for feature selection.
However, existing MB learning algorithms often fail to identify some critical features in real …

The fusion of causal models with deep learning introducing increasingly intricate data sets,
such as the causal associations within images or between textual components, has surfaced …

In online advertising, marketing mix modeling (MMM) is employed to predict the gross
merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget …

The rise in cyber attacks on cyber-physical critical infrastructures, like water treatment
networks, is evidenced by the growing frequency of breaches and the evolving …

The growing availability and importance of time series data across various domains,
including environmental science, epidemiology, and economics, has led to an increasing …

With the rapid growth of massive time-series data, inferring temporal Bayesian structures
based on causation from data—Temporal Causal Discovery (TCD)—has become an …