Novel Regression

Novel regression research focuses on improving the accuracy, efficiency, and interpretability of regression models, particularly in challenging scenarios with limited data, high dimensionality, or complex relationships. Current efforts explore diverse model architectures, including deep neural networks (DNNs), recurrent neural networks (RNNs), and large language models (LLMs), alongside innovative techniques like active learning, Bayesian methods, and post-processing for fairness and uncertainty quantification. These advancements are significant for various fields, enabling more robust predictions in applications ranging from materials science and medical imaging to robotics and environmental modeling. The development of efficient and reliable regression methods continues to be a crucial area of research with broad practical implications.