Model Averaging

Model averaging, a technique that combines predictions from multiple machine learning models, aims to improve prediction accuracy, robustness, and uncertainty quantification compared to using a single model. Current research focuses on optimizing model averaging within various contexts, including federated learning (where models are trained on decentralized data), Bayesian neural networks, and handling class imbalances or out-of-distribution data. These advancements enhance the performance and stability of machine learning models across diverse applications, from image classification and natural language processing to medical image analysis and causal inference.