Regression Task

Regression tasks, aiming to predict continuous output values based on input features, are a cornerstone of machine learning, with applications spanning diverse fields. Current research emphasizes improving model robustness and uncertainty quantification, particularly in the face of noisy data, imbalanced datasets, and limited training samples; this includes exploring advanced architectures like transformers and Bayesian methods, as well as novel data augmentation and hyperparameter optimization techniques. These advancements are crucial for enhancing the reliability and trustworthiness of regression models in critical applications, such as engineering, scientific modeling, and autonomous systems, where accurate predictions and well-calibrated uncertainty estimates are paramount.