Learning to Optimize in Swarms

Learning to optimize has emerged as a powerful framework for various optimization and machine learning tasks. Current such meta-optimizers often learn in the space of continuous optimization algorithms that are point-based and uncertainty-unaware. To overcome the limitations, we propose a meta-optimizer that learns in the algorithmic space of both point-based and population-based optimization algorithms.