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parallel multi-objective bayesian optimization

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Review for NeurIPS paper: Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

Neural Information Processing SystemsJan-25-2025, 15:33:27 GMT

Summary and Contributions: Multi-objective optimization (MOO) problems involve more than one objective function that are to be minimized or maximized. For non-trivial instances of MOO problems, no unique solution exists that simultaneously optimizes all objectives. In that case, the aim to identify the set of Pareto optimal solutions of the problem. Bayesian Optimization (BO) approaches rely on acquisition functions (AF), to evaluate promising query points for function evaluations. BO approaches for MOO require to define AFs that are applicable to the notion of Pareto optimality.

hypervolume improvement, neurips paper, parallel multi-objective bayesian optimization, (7 more...)

Neural Information Processing Systems

Technology: Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.64)

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