The paper proposes the first multi-step lookahead acquisition functions for multi-objective Bayesian optimization, using hypervolume improvement as the reward scalarization, and reports improved Pareto-front hypervolume on real-world benchmarks.
NMMO-Nested: O(KN 2H 2M ) Where M is the size of the discretized input space used for the nested optimization
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Non-Myopic Multi-Objective Bayesian Optimization
The paper proposes the first multi-step lookahead acquisition functions for multi-objective Bayesian optimization, using hypervolume improvement as the reward scalarization, and reports improved Pareto-front hypervolume on real-world benchmarks.