Multi-objective Asynchronous Successive Halving

Understand

Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e.g., accuracy) of machine learning models.

• However, in a plethora of real-world applications, accuracy is only one of the multiple -- often conflicting -- performance criteria, necessitating the adoption of a multi-objective (MO) perspective.
• While the literature on MO optimization is rich, few prior studies have focused on HPO.
• In this paper, we propose algorithms that extend asynchronous successive halving (ASHA) to the MO setting.