Abstract Neural Temporal Point Processes (TPPs) are the prevalent paradigm for modeling
continuous-time event sequences, such as user activities on the web and financial …

Large language models (LLMs), such as GPT series models, have received substantial
attention due to their impressive capabilities for generating and understanding human-level …

Causal effect estimation from networked observational data encounters notable challenges,
primarily hidden confounders arising from network structure, or spillover effects that …

A further understanding of cause and effect within observational data is critical across many
domains, such as economics, health care, public policy, web mining, online advertising, and …

Enhancing the generalization performance of neural networks remains a formidable
challenge, due to the model selection trade-off between training error and generalization …

Although the widespread use of AI systems in today's world is growing, many current AI
systems are found vulnerable due to hidden bias and missing information, especially in the …

Machine learning and causal inference have gained significant attention in both academia
and industry for the past decades, but they have been mainly treated as separate research …

In this chapter, we provide a comprehensive review of causal inference methods for the
causal effect estimation task under the potential outcome framework, one of the well-known …

Various probabilistic time series forecasting models have sprung up and shown remarkably
good performance. However, the choice of model highly relies on the characteristics of the …

The expanding instrumentation of processes throughout society with sensors yields a
proliferation of time series data that may in turn enable important applications, eg, related to …