Algorithm Dependent Generalization

Algorithm-dependent generalization in machine learning focuses on understanding how the choice of training algorithm impacts a model's ability to generalize to unseen data, moving beyond traditional algorithm-agnostic bounds. Current research investigates this interplay using various architectures, including transformers and graph neural networks, often within the context of specific tasks like combinatorial optimization or AUPRC maximization. This research aims to provide tighter, more accurate generalization bounds and improve model performance by explicitly considering the algorithm's role in the learning process, leading to more robust and reliable machine learning systems.