Decision trees are renowned for their interpretability capability to achieve high predictive
performance, especially on tabular data. Traditionally, they are constructed through …

The increasing deployment of artificial intelligence systems in various applications has
intensified concerns regarding their compliance with stringent privacy regulations …

Learning with a limited number of labeled data is a central problem in real-world
applications of machine learning, as it is often expensive to obtain annotations. To deal with …

Learning effective representations from raw data is crucial for the success of deep learning
methods. However, in the tabular domain, practitioners often prefer augmenting raw column …

To evaluate the hallucination tendencies of state-ofthe-art language models is crucial for
improving their reliability and applicability across various domains. This article presents a …

Ensuring the semantic validity of text generated through artificial intelligence remains a
significant challenge, particularly as applications in various domains demand higher …

Abstract Knowledge graphs have become essential tools for structuring and representing
complex information across various domains, enabling advanced applications such as …

Evaluating the effectiveness of ChatGPT in scrutinizing and debunking misinformation
embedded within social media memes reveals significant potential for AI-driven fact …

Generative artificial intelligence systems, such as OpenAI ChatGPT and Anthropic Claude,
have shown remarkable capabilities in generating human-like text responses, raising …

Recent advancements in natural language processing have highlighted the critical
importance of efficiently updating pre-trained models with domain-specific knowledge …