DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
Pareto set learning for expensive multi-objective optimization
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CoAction applies a transformer encoder with per-task embeddings to jointly solve multiple multi-objective optimization problems by capturing cross-task correlations.
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Large Language Models as Amortized Pareto-Front Generators for Constrained Bi-Objective Convex Optimization
DIPS fine-tunes LLMs to output ordered feasible decision vectors approximating Pareto fronts for constrained bi-objective convex problems, reaching 95-98% normalized hypervolume with 0.16s inference.
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CoAction: Cross-task Correlation-aware Pareto Set Learning
CoAction applies a transformer encoder with per-task embeddings to jointly solve multiple multi-objective optimization problems by capturing cross-task correlations.