Parameterized Regime

Parameterized regimes in machine learning analyze how model performance changes with the relationship between the number of data points and model parameters (under-, over-, or proportionally parameterized). Current research focuses on understanding generalization error and convergence properties across these regimes, employing various models including kernel methods, deep neural networks (e.g., ResNets), and graph neural networks, and investigating algorithms like the Deep Ritz Method and federated learning. This research is crucial for optimizing model design and training strategies, improving performance, and addressing issues like catastrophic forgetting and fairness in practical applications. A deeper understanding of these regimes is essential for building more robust and reliable machine learning systems.