Provable Generalization

Provable generalization in machine learning focuses on developing models and training methods that reliably generalize to unseen data, a crucial challenge hindering the widespread adoption of many powerful models. Current research investigates this through various lenses, including analyzing the internal mechanisms of successful models like transformers and graph neural networks, developing novel algorithms like coreset selection for efficient training, and addressing issues like task confounders and data heterogeneity in meta-learning and federated learning settings. These efforts aim to provide theoretical guarantees on generalization performance, leading to more robust and reliable machine learning systems across diverse applications, from complex scientific modeling to real-world decision-making.