Investigating the impact of multicollinearity on statistical and machine learning models, with a focus on diagnosis, model stability, predictive performance, and feature attribution.

Investigating Bayesian Active Learning for label-efficient LLM fine-tuning through uncertainty and diversity-based sample selection.