Peter Bachman

Learning What to Evaluate: Correlation-Aware Decoupling for Multiobjective Bayesian Optimization

This work presents a new approach that leverages a multitask GP model to jointly learn all objectives and constraints, and proposes a total correlation metric that enables identifying an ${optimal}$ subset of objectives and constraints to be evaluated at every round, even under uniform evaluation costs.