Robust Generalization

Robust generalization in machine learning focuses on developing models that perform reliably not only on the data they were trained on, but also on unseen data, including data with noise or distortions. Current research explores diverse techniques, such as specialized optimizers, ensemble methods that balance sharpness and diversity, and adversarial training approaches that mitigate overfitting, often applied to deep neural networks, graph neural networks, and vision-language models. These advancements are crucial for deploying machine learning models in real-world applications where encountering unexpected data is inevitable, improving the reliability and safety of AI systems across various domains.