Related papers: Amortized Active Generation of Pareto Sets

Amortized Active Generation of Pareto Sets

URL: http://arxiv.org/abs/2510.21052v2
Date: Mon, 03 Nov 2025 05:27:43 GMT
Title: Amortized Active Generation of Pareto Sets
Authors: Daniel M. Steinberg, Asiri Wijesinghe, Rafael Oliveira, Piotr Koniusz, Cheng Soon Ong, Edwin V. Bonilla,
Abstract summary: A-GPS is a new framework for online discrete black-box multi-objective optimization.<n>Method employs a class probability estimator to predict non-dominance relations.<n>We show that this non-dominance CPE implicitly estimates the probability of hypervolume improvement.
Score: 48.56811624922571
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We introduce active generation of Pareto sets (A-GPS), a new framework for online discrete black-box multi-objective optimization (MOO). A-GPS learns a generative model of the Pareto set that supports a-posteriori conditioning on user preferences. The method employs a class probability estimator (CPE) to predict non-dominance relations and to condition the generative model toward high-performing regions of the search space. We also show that this non-dominance CPE implicitly estimates the probability of hypervolume improvement (PHVI). To incorporate subjective trade-offs, A-GPS introduces preference direction vectors that encode user-specified preferences in objective space. At each iteration, the model is updated using both Pareto membership and alignment with these preference directions, producing an amortized generative model capable of sampling across the Pareto front without retraining. The result is a simple yet powerful approach that achieves high-quality Pareto set approximations, avoids explicit hypervolume computation, and flexibly captures user preferences. Empirical results on synthetic benchmarks and protein design tasks demonstrate strong sample efficiency and effective preference incorporation.

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