用粒子演化方法同时优化多个目标，提升解的多样性和质量。
A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks
- 用粒子逼近帕累托集，通过梯度下降动态调整解分布。
- 在多任务学习中实现更高超体积值和更优解多样性。
- 适合需要平衡多个目标的机器学习场景，如多任务优化。
多目标学习中，帕累托集学习（PSL）是一种高效获取完整最优解的方法。一组最优解近似帕累托集，其映射为客观空间中的密集点集。然而，现有方法面临挑战：如何在最大化超体积值的同时保持解的多样性。本文提出一种新方法，利用斯蒂恩-变分梯度下降（SVGD）逼近整个帕累托集。SVGD通过函数梯度下降推动一组粒子向帕累托集收敛，有助于解的收敛与多样化。此外，我们采用多样化的梯度方向策略，构建统一的多目标优化框架，并引入退火调度以增强稳定性。提出SVH-MOL方法，在多目标问题与多任务学习上进行了广泛实验，验证了其优越性能。
原文摘要 · Abstract (English)
Pareto Set Learning (PSL) is popular as an efficient approach to obtaining the complete optimal solution in Multi-objective Learning (MOL). A set of optimal solutions approximates the Pareto set, and its mapping is a set of dense points in the Pareto front in objective space. However, some current methods face a challenge: how to make the Pareto solution is diverse while maximizing the hypervolume value. In this paper, we propose a novel method to address this challenge, which employs Stein Variational Gradient Descent (SVGD) to approximate the entire Pareto set. SVGD pushes a set of particles towards the Pareto set by applying a form of functional gradient descent, which helps to converge and diversify optimal solutions. Additionally, we employ diverse gradient direction strategies to thoroughly investigate a unified framework for SVGD in multi-objective optimization and adapt this framework with an annealing schedule to promote stability. We introduce our method, SVH-MOL, and validate its effectiveness through extensive experiments on multi-objective problems and multi-task learning, demonstrating its superior performance.
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