Arbitrary Machine Learning Model

Arbitrary machine learning models encompass a broad range of predictive methods, focusing on developing techniques applicable to any underlying model architecture. Current research emphasizes improving model-agnostic uncertainty quantification, including methods for constructing confidence intervals and prediction intervals regardless of the base model. This work is crucial for enhancing the reliability and trustworthiness of machine learning predictions across diverse scientific and engineering applications, particularly in situations where the underlying data generating process is complex or poorly understood. Furthermore, research explores using powerful models like large language models to post-hoc correct or improve the performance of other, potentially less expensive, models.