Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields
of study with significant implications for artificial intelligence. Despite their distinct origins …

Causality lays the foundation for the trajectory of our world. Causal inference (CI), which
aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial …

Large Language Models (LLMs) have shown impressive performance in natural language
tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for …

As personalized recommendation systems become vital in the age of information overload,
traditional methods relying solely on historical user interactions often fail to fully capture the …

The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis
of high-fidelity video content guided by textual descriptions. Despite this significant progress …

Accurate workload forecasting is critical for efficient resource management in cloud
computing systems, enabling effective scheduling and autoscaling. Despite recent advances …

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 …

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

The right to be forgotten (RTBF) seeks to safeguard individuals from the enduring effects of
their historical actions by implementing machine-learning techniques. These techniques …

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