Distance Based Regularization

Distance-based regularization is a technique used to improve the performance and robustness of machine learning models by incorporating information about the distances between data points or features into the model's training process. Current research focuses on applying this technique to diverse areas, including graph embeddings, generative models, and deep neural networks for tasks like node classification and brain age prediction, often employing variational autoregressive networks or adapting existing algorithms like Skip-Gram Negative Sampling. This approach addresses challenges such as generalization, handling of outliers and noise, and efficient training in resource-constrained environments like federated learning, ultimately leading to more accurate, reliable, and scalable models across various applications.