Mixed Effect

Mixed effects modeling investigates how different factors influence an outcome, accounting for both fixed and random effects. Current research focuses on understanding the impact of various factors across diverse applications, employing diverse models such as deep neural networks, logistic regression, and random feature models, often within the context of interpretability and bias mitigation. This field is crucial for advancing understanding in various domains, from improving AI systems and human-computer interaction to enhancing medical diagnoses and optimizing industrial processes.