Smart healthcare has achieved significant progress in recent years. Emerging artificial
intelligence (AI) technologies enable various smart applications across various healthcare …

In recent years, gesture recognition and speech recognition, as important input methods in
Human–Computer Interaction (HCI), have been widely used in the field of virtual reality. In …

Reasoning, as an essential ability for complex problem-solving, can provide back-end
support for various real-world applications, such as medical diagnosis, negotiation, etc. This …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

The past decade has witnessed dramatic gains in natural language processing and an
unprecedented scaling of large language models. These developments have been …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …

Some argue scale is all what is needed to achieve AI, covering even causal models. We
make it clear that large language models (LLMs) cannot be causal and give reason onto …

While personalization increases the utility of recommender systems, it also brings the issue
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …

Models trained with counterfactually augmented data learn representations of the causal
structure of tasks, enabling robust generalization. However, high-quality counterfactual data …

Counterfactual examples have proven to be valuable in the field of natural language
processing (NLP) for both evaluating and improving the robustness of language models to …