<ul><li>Pareto Set Learning (PSL) is efficient in Multi-objective Learning (MOL) to obtain the complete optimal solution.</li><li>Approach addresses the challenge of making diverse Pareto solutions while maximizing hypervolume value.</li><li>Proposed method SVH-MOL uses Stein Variational Gradient Descent (SVGD) to approximate entire Pareto set.</li><li>Method validated through experiments on multi-objective problems and multi-task learning, showing superior performance.</li></ul>

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

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