<ul><li>Researchers propose an efficient first-order optimization method for multi-objective learning under preference guidance.</li><li>The problem is framed as a semivectorial bilevel optimization problem, optimizing a pre-defined preference function with weakly Pareto optimal model parameters.</li><li>To solve the problem, the multi-objective constraints are converted to a single-objective constraint using a merit function with an easy-to-evaluate gradient.</li><li>The proposed method is shown to effectively find preference-guided optimal solutions in various synthetic and real-world problems.</li></ul>

Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance

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