🏢 CISPA

Adaptive gradient methods using AdaGrad stepsizes achieve optimal convergence rates for convex composite optimization problems, handling inexact oracles, acceleration, and variance reduction without n…

Dynamic rescaling boosts GNN training by controlling layer learning speeds and balancing networks, leading to faster training and improved generalization, especially on heterophilic graphs.