Generalization Performance

Generalization performance in machine learning focuses on a model's ability to accurately predict outcomes on unseen data, a crucial aspect for real-world applications. Current research investigates this through various lenses, including mitigating overfitting in self-supervised and federated learning, improving the robustness of models to out-of-distribution data (e.g., using dropout or orthogonal regularization), and enhancing the efficiency of fine-tuning large pre-trained models (e.g., via low-rank adaptation). Understanding and improving generalization is vital for building reliable and adaptable AI systems across diverse domains, impacting fields from image recognition and natural language processing to control of biological neural networks.