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Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent

NeurIPS 2021 · Optimization › Optimizers · approach: Stein variational gradient descent · 11 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use Stein repulsive kernel force (MOO-SVGD) instead of Langevin noise (MOO-LD)multi-objective sampling · MaF1 tri-objectivehypervolume: 0.2613 -> 0.5867Helped
MOO-SVGD vs EPO with preference vectorsgradient-based MOO · MaF1 tri-objectivehypervolume: 0.5714 -> 0.5867Helped
MOO-SVGD vs EPO, PF-SMG, COSMOS and hypervolume optimizationZDT1-3 · ZDT bi-objectivehypervolume over wall-clock timeHelped
MOO-SVGD vs EPO and PF-SMG on fairness trade-offlinear classifier · accuracy vs CV scorePareto front qualityHelped
vary kernel bandwidth h and number of particles (ablation)MOO-SVGD · ZDT1final hypervolumeMixed
vary temperature alpha (ablation)MOO-SVGD · ZDT1final hypervolumeMixed

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