Amortized Active Generation of Pareto Sets
Daniel Steinberg, Asiri Wijesinghe, Rafael Oliveira, Piotr Koniusz, Cheng Soon Ong, Edwin Bonilla
We propose a new framework called active generation of Pareto sets (A-GPS) for online discrete black-box multi objective optimization (MOO) that learns a generative model of the Pareto set and supports a-posteriori preference conditioning. Our method actively learns a generative model conditioned on high-performance regions (active generation) using amortized variational inference. It uses a class probability estimator (CPE) for predicting Pareto-optimality and conditioning the generative model. Furthermore, motivated by discrete/mixed design problems where we must balance multiple competing objectives, it introduces preference direction vectors to capture subjective trade-offs. Thus, at each iteration, we update a generative model conditioned on Pareto set membership _and_ alignment with preference directions. Our method yields high-quality Pareto set approximations using only simple CPE guidance, avoids hyper-volume computation, and supports sampling at arbitrary trade-off points without retraining. Empirical results on synthetic functions and protein design benchmarks demonstrate strong sample efficiency and effective incorporation of users' preferences.