A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks

• URL: http://arxiv.org/abs/2506.06715v2
• Date: Tue, 10 Jun 2025 04:36:06 GMT
• Title: A Framework for Controllable Multi-objective Learning with Annealed Stein Variational Hypernetworks
• Authors: Minh-Duc Nguyen, Dung D. Le,
• Abstract summary: A set of optimal solutions approximates the Pareto set.<n>Some current methods face a challenge: how to make the solution diverse while maximizing the hypervolume value.<n>In this paper, we propose a novel method to address this challenge, which employs Stein Variational Gradient Descent.
• Score: 4.166623313248681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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