Parameterized Model

Parameterized models, particularly overparameterized ones with more parameters than data points, are a central focus in modern machine learning research, aiming to understand their surprising generalization capabilities despite apparent overfitting. Current research investigates the impact of model size, data characteristics, and training algorithms (like stochastic gradient descent and its variants, including accelerated and adaptive methods) on model performance, focusing on architectures such as deep neural networks and kernel regression. This research is crucial for improving the efficiency and robustness of machine learning models across diverse applications, from renewable energy prediction to robust classification in noisy environments.